Physiologically based pharmacokinetic modeling in drug discovery and development: A pharmaceutical industry perspective

Simultaneous Estimation of fm and FG Values Directly from Clinical Drug-Drug Interaction Study Data

During drug development, the design, interpretation and risk assessment of drug-drug interaction (DDI) are generally performed with physiologically-based pharmacokinetic (PBPK) modelling. Critical parameters are the hepatic metabolic fraction (fm) and intestinal availability (FG) which are commonly informed by clinical data. In this study, two methods for the simultaneous estimation of these parameters are proposed which utilize the distinctive changes in substrate's plasma concentration profiles in response to inhibition of intestinal and hepatic enzymes. The two-dimensional DDI (2D-DDI) method estimates the fm and FG values directly from the ratios of area-under-curve (AUCR) and maximum concentration (CmaxR), while the population PBPK method utilizes the full concentration-time data of a substrate without or with an inhibitor. The utility of both methods was demonstrated for a broad range of > 50,000 virtual and six actual CYP3A substrates. The 2D-DDI method is fast, reliable, and does not require a priori PBPK model development. The population PBPK method can estimate the population parameters and inter-individual variabilities of fm and FG and is applicable to more complex DDIs (e.g., multiple pathways/dynamic inhibitor concentration-time profiles) without the need for IV data. Like other approaches, both methods show an increasing uncertainty for substrates with high hepatic extraction and sensitivity to the assumed degree of enzyme inhibition. While both methods were evaluated for CYP3A substrates, the methodology equally applies to other enzymes. Additionally, this study provides guidance for clinical DDI study design to facilitate robust DDI extrapolation necessary to inform drug labels on concomitant medications in lieu of clinical trials.

Using Physiologically Based Pharmacokinetic Models for Assessing Pharmacokinetic Drug-Drug Interactions in Patients with Chronic Heart Failure Taking Narrow Therapeutic Window Drugs

Background: Pharmacotherapy of chronic heart failure (CHF) with a reduced ejection fraction includes a combination of drugs. Often, different groups of drugs are added together for the treatment of concomitant conditions, such as statins, anticoagulants, antiplatelet agents, and cardiac glycosides, which have a narrow therapeutic window. Increased medication intake is a prerequisite for the increased risk of potential adverse drug-drug interactions (DDI), especially those occurring at the pharmacokinetic level. The main objectives of this study are to identify the most common potential pharmacokinetic drug-drug interactions (pPKDDIs), to explore more complex cases, and to simulate and analyze them with appropriate software. Methods: The data selected for the simulations were collected over a two-year period from January 2014 to December 2015. Identification of the pPKDDIs was carried out using Lexicomp Drug interaction, while simulations were performed with Simcyp software (V20, R1). Results: The most common pharmacokinetic mechanisms responsible for the occurrence of drug-drug interactions in the selected drugs with narrow therapeutic windows are those featuring the cytochrome isoforms CYP3A4 and 2C9 and the efflux pump-P-glycoprotein (P-gp). Simulations with the available data in Simcyp software showed possibilities to analyze and evaluate pPKDDIs, which would be difficult to assess without appropriate software, as well as ways to manage them. Conclusions: The frequency and complexity of pPKDDIs in patients with cardiovascular disease are high. Therefore, such patients require a specific approach to reduce these risks as well as to optimize the therapy. An appropriate PBPK software with the necessary database would be suitable in these cases.

A new paradigm for drug discovery in the treatment of complex diseases: drug discovery and optimization

In the past, the drug research and development has predominantly followed a "single target, single disease" model. However, clinical data show that single-target drugs are difficult to interfere with the complete disease network, are prone to develop drug resistance and low safety in clinical use. The proposal of multi-target drug therapy (also known as "cocktail therapy") provides a new approach for drug discovery, which can affect the disease and reduce adverse reactions by regulating multiple targets. Natural products are an important source for multi-target innovative drug development, and more than half of approved small molecule drugs are related to natural products. However, there are many challenges in the development process of natural products, such as active drug screening, target identification and preclinical dosage optimization. Therefore, how to develop multi-target drugs with good drug resistance from natural products has always been a challenge. This article summarizes the applications and shortcomings of related technologies such as natural product bioactivity screening, clarify the mode of action of the drug (direct/indirect target), and preclinical dose optimization. Moreover, in response to the challenges faced by natural products in the development process and the trend of interdisciplinary and multi-technology integration, and a multi-target drug development strategy of "active substances - drug action mode - drug optimization" is proposed to solve the key challenges in the development of natural products from multiple dimensions and levels.

Physiologically based pharmacokinetic modeling of midostaurin and metabolites at steady-state to bridge drug interaction scenarios in lieu of clinical trials

Midostaurin and its active metabolites are substrates, mixed inhibitors/inducers of cytochrome P450 (CYP)3A4. The main objective of this study was to develop/refine a physiologically based pharmacokinetic (PBPK) model that incorporated recent clinical drug-drug interaction (DDI) data with midazolam after multiple dosing, to qualify the pharmacokinetic (PK) model simulations of midostaurin and its metabolites, and to apply it to predict untested clinical DDI scenarios with potential comedications. In this study, Simcyp PBPK model of midostaurin and its 2 metabolites was refined from a previously published model associated with endogenous biomarker 4β-hydroxycholesterol data through further optimization of CYP3A4 inhibition/induction potency and was qualified to simulate midostaurin steady-state PK. The incorporation of these parameters enabled DDI predictions of high midostaurin doses on the PK of midazolam and oral contraceptives containing ethinyl estradiol. Additionally, scaling factors for in vitro breast cancer resistance protein and the organic anion transporting polypeptide (OATP1B) inhibition were applied to account for the observed single-dose DDI with rosuvastatin and further extrapolated to predict steady-state DDI with other OATP1B drug substrates. The overall prediction results showed minimal impact of midostaurin at high doses on CYP3A substrates or an effect on the exposure of OATP1B substrates. In summary, the midostaurin PBPK model was retrospectively refined, requalified, and used to simulate the steady-state perpetrator DDI of midostaurin and its metabolites. This PBPK modeling approach and the resulting model predictions were implemented into the midostaurin product label (up to 100 mg twice a day) without the need for confirmatory clinical studies. SIGNIFICANCE STATEMENT: The manuscript describes how a midostaurin PBPK model was updated, verified, and applied to untested scenarios by a predict-learn-confirm cycle as new clinical data become available. It also provides a learning experience of prospective prediction by utilizing endogenous biomarker 4β-hydroxycholesterol to evaluate a complex CYP3A4-mediated drug interaction.

Clinical and Physiologically Based Pharmacokinetic Model Evaluations of Adagrasib Drug-Drug Interactions

Adagrasib is a potent, highly selective, orally available, small molecule, covalent inhibitor of G12C mutated KRAS. As both a substrate and strong inhibitor of cytochrome P450 (CYP) 3A4, adagrasib inhibits its own CYP3A4-mediated metabolism following multiple dosing, resulting in time-dependent drug-drug interaction (DDI) liabilities. A physiologically-based pharmacokinetic (PBPK) model was developed and verified using a combination of physicochemical, in vitro and clinical pharmacokinetic (PK) data from healthy volunteers and cancer patients. The PBPK model well-described the single and multiple-dose adagrasib PK data as well as DDI data with itraconazole, rifampin, midazolam, warfarin, dextromethorphan, and digoxin, with model predictions within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested scenarios including the clinical victim and perpetrator DDI liabilities at the approved dosing regimen of 600 mg twice daily (b.i.d.) in cancer patients. Strong, moderate, and weak inhibitors of CYP3A4 are predicted to have a negligible effect on the steady-state exposure of adagrasib 600 mg b.i.d. resulting from the significant inactivation of CYP3A4 by adagrasib. Additionally, strong and moderate inducers of CYP3A4 are predicted to decrease adagrasib exposure by 68% and 22%, respectively. As a perpetrator, adagrasib 600 mg b.i.d. is predicted to be a strong inhibitor of CYP3A4, a moderate inhibitor of CYP2C9 and CYP2D6, and an inhibitor of P-glycoprotein (P-gp). These results successfully supported regulatory interactions with the United States Food and Drug Administration regarding dosing recommendations for when adagrasib is used concomitantly with other medications, supporting a range of label claims in lieu of clinical trials.

Evaluation of BCRP-Related DDIs Between Methotrexate and Cyclosporin A Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND AND OBJECTIVE

This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.

METHODS

PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs. A qualification of an introduced empirical uniform in vitro scaling factor of Ki values for transporter inhibition by CsA was conducted by using a previously developed model of rosuvastatin (sensitive index BCRP substrate), and assessing if corresponding DDI ratios were well captured.

RESULTS

Within the simulated DDI scenarios for MTX in the presence of CsA, the developed models could capture the observed changes in PK parameters as changes in the area under the curve ratios (area under the curve during DDI/area under the curve control) of 1.30 versus 1.31 observed and the DDI peak plasma concentration ratios (peak plasma concentration during DDI/peak plasma concentration control) of 1.07 versus 1.28 observed. The originally reported in vitro Ki values of CsA were scaled with the uniform qualified scaling factor for their use in the in vivo DDI simulations to correct for the low intracellular unbound fraction of the CsA effector concentration. The resulting predicted versus observed ratios of peak plasma concentration and area under the curve DDI ratios with MTX were 0.82 and 0.99, respectively, indicating adequate model accuracy and choice of a scaling factor to capture the observed DDI.

CONCLUSIONS

All models have been comprehensively documented and made publicly available as tools to support the drug development and clinical research community and further community-driven model development.

Evaluation of Solubility-Limited Absorption as a Surrogate to Predicting Positive Food Effect of BCS II/IV Drugs

INTRODUCTION AND OBJECTIVE

Physiologically based pharmacokinetic (PBPK) models are increasingly used to predict food effect (FE) but model parameterization is challenged by in vitro-in vivo (IVIV) disconnect and/or parameter nonidentifiability. To overcome these issues, we propose a simplified PBPK model, in which all solubility-driven processes are lumped into a single parameter, solubility, which is optimized against observed concentration-time data.

METHODS

A set of commercially available biopharmaceutical classification system (BCS) II/IV compounds was selected to measure the solubility in a fasted state simulated intestinal fluid (FaSSIF) medium. The compounds were ranked from the lowest to the highest dose-adjusted FaSSIF solubility (FaSSIF/D) value and subdivided into three areas based on an upper and a lower limit: drugs with FaSSIF/D > upper limit having no FE, drugs with FaSSIF/D < lower limit having FE, and drugs between the limits said to be in the sensitivity range (SR), for which we tested the hypothesis that solubility-limited absorption (SLA) identified by simplified PBPK model can reliably predict positive FE if their exposures are not impacted by gut efflux or gut metabolism.

RESULTS

We demonstrate, using a subset of drugs within SR for which PBPK models were available, that drugs with SLA exhibited a positive FE, while those with no SLA did not show FE.

CONCLUSIONS

This proposal allows for a reliable binary prediction of FE to enable timely decisions on the need for pilot FE studies as well as the timing of pivotal FE studies.

Opportunities and Challenges of Hepatic Impairment Physiologically Based Pharmacokinetic Modeling in Drug Development-An IQ Perspective

PBPK models are gaining special interest as a drug development tool to estimate the effect of intrinsic and extrinsic factors on drug pharmacokinetics. The IQ Consortium Clinical Pharmacology Organ Impairment Working Group conducted a survey across IQ Consortium member pharmaceutical companies to understand current practices on how PBPK is used in understanding the effect of hepatic impairment (HI) on drug disposition and its impact on clinical development. Responses from 21 participants indicated that most organizations (~86%) are already using PBPK models for HI assessment. The survey results indicate that PBPK models have been influential in optimizing the design of dedicated HI study with 57% of respondents using PBPK models to inform the design elements of dedicated HI studies, and the majority of these respondents using the PBPK model to support internal decision making regarding the HI study. Additionally, the PBPK model was used by 62% of the respondents to predict drug plasma protein binding. Despite common usage of the PBPK models by drug developers, 14.3% of the respondents discussed their PBPK modeling strategy with regulatory agencies with only two cases where the regulators accepted the PBPK model. In conclusion, although the use of PBPK models to support regulatory decisions regarding drug use in HI is currently limited, its future is promising, and the success of such models needs collaboration between regulators and drug developers to shrink the knowledge gap in the use of PBPK as an impactful tool for drug development in patients with HI.

Digital twin-enhanced three-organ microphysiological system for studying drug pharmacokinetics in pregnant women

Background

Pregnant women represent a vulnerable group in pharmaceutical research due to limited knowledge about drug metabolism and safety of commonly used corticosteroids like prednisone due to ethical and practical constraints. Current preclinical models, including animal studies, fail to accurately replicate human pregnancy conditions, resulting in gaps in drug safety and pharmacokinetics predictions. To address this issue, we used a three-organ microphysiological system (MPS) combined with a digital twin framework, to predict pharmacokinetics and fetal drug exposure.

Methods

The here shown human MPS integrated gut, liver, and placenta models, interconnected via the corresponding vasculature. Using prednisone as a model compound, we simulate oral drug administration and track its metabolism and transplacental transfer. To translate the generated data from MPS to human physiology, computational modelling techniques were developed.

Results

Our results demonstrate that the system maintains cellular integrity and accurately mimics in vivo drug dynamics, with predictions closely matching clinical data from pregnant women. Digital twinning closely aligned with the generated experimental data. Long-term exposure simulations confirmed the value of this integrated system for predicting the non-toxic metabolization of prednisone.

Conclusion

This approach may provide a potential non-animal alternative that could contribute to our understanding of drug behavior during pregnancy and may support early-stage drug safety assessment for vulnerable populations.

DigiLoCS: A leap forward in predictive organ-on-chip simulations

Digital twins, driven by data and mathematical modelling, have emerged as powerful tools for simulating complex biological systems. In this work, we focus on modelling the clearance on a liver-on-chip as a digital twin that closely mimics the clearance functionality of the human liver. Our approach involves the creation of a compartmental physiological model of the liver using ordinary differential equations (ODEs) to estimate pharmacokinetic (PK) parameters related to on-chip liver clearance. The objectives of this study were twofold: first, to predict human clearance values, and second, to propose a framework for bridging the gap between in vitro findings and their clinical relevance. The methodology integrated quantitative Organ-on-Chip (OoC) and cell-based assay analyses of drug depletion kinetics and is further enhanced by incorporating an OoC-digital twin model to simulate drug depletion kinetics in humans. The in vitro liver clearance for 32 drugs was predicted using a digital-twin model of the liver-on-chip and in vitro to in vivo extrapolation (IVIVE) was assessed using time series PK data. Three ODEs in the model define the drug concentrations in media, interstitium and intracellular compartments based on biological, hardware, and physicochemical information. A key issue in determining liver clearance appears to be the insufficient drug concentration within the intracellular compartment. The digital twin establishes a connection between the hardware chip structure and an advanced mapping of the underlying biology, specifically focusing on the intracellular compartment. Our modelling offers the following benefits: i) better prediction of intrinsic liver clearance of drugs compared to the conventional model and ii)explainability of behaviour based on physiological parameters. Finally, we illustrate the clinical significance of this approach by applying the findings to humans, utilising propranolol as a proof-of-concept example. This study stands out as the biggest cross-organ-on-chip platform investigation to date, systematically analysing and predicting human clearance values using data obtained from various in vitro liver-on-chip systems. Accurate prediction of in vivo clearance from in vitro data is important as inadequate understanding of the clearance of a compound can lead to unexpected and undesirable outcomes in clinical trials, ranging from underdosing to toxicity. Physiologically based pharmacokinetic (PBPK) model estimation of liver clearance is explored. The aim is to develop digital twins capable of determining better predictions of clinical outcomes, ultimately reducing the time, cost, and patient burden associated with drug development. Various hepatic in vitro systems are compared and their effectiveness for predicting human clearance is investigated. The developed tool, DigiLoCs, focuses explicitly on accurately describing complex biological processes within liver-chip systems. ODE-constrained optimisation is applied to estimate the clearance of compounds. DigiLoCs enable differentiation between active biological processes (metabolism) and passive processes (permeability and partitioning) by incorporating detailed information on compound-specific characteristics and hardware-specific data. These findings signify a significant stride towards more accurate and efficient drug development methodologies.

Physiologically Based Pharmacokinetic Modeling of Cannabidiol, Delta-9-Tetrahydrocannabinol, and Their Metabolites in Healthy Adults After Administration by Multiple Routes

The two most extensively studied cannabinoids, cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), are used for myriad conditions. THC is predominantly eliminated via the cytochromes P450 (CYPs), whereas CBD is eliminated through both CYPs and UDP-glucuronosyltransferases (UGTs). The fractional contributions of these enzymes to cannabinoid metabolism have shown conflicting results among studies. Physiologically based pharmacokinetic (PBPK) models for CBD and THC and for drug-drug interaction studies involving CBD or THC as object drugs were developed and verified to improve estimates of these contributions. First, physicochemical and pharmacokinetic parameters for CBD, THC, and their metabolites (7-OH-CBD, 11-OH-THC, and 11-COOH-THC) were obtained from the literature or optimized. Second, PBPK base models were developed for CBD and THC after intravenous administration. Third, beginning with the intravenous models, absorption models were developed for CBD after oral and oromucosal spray administration and for THC after oral, inhalation, and oromucosal spray administration. The full models well-captured the area under the concentration-time curve (AUC) and peak concentration (Cmax) of CBD and THC from the verification dataset. Predicted AUC and Cmax for CBD and 7-OH-CBD were within two-fold of the observed data. For THC, 11-OH-THC, and 11-COOH-THC, 100%, 100%, and 83% of the predicted AUC values were within two-fold, respectively, of the observed values; 100%, 92%, and 94% of the predicted Cmax values, respectively, were within two-fold of the observed values. The verified models could be used to help address critical public health needs, including assessing potential drug interaction risks involving CBD and THC.

Physiologically based pharmacokinetic modeling of small molecules: How much progress have we made?

Physiologically based pharmacokinetic (PBPK) models of small molecules have become mainstream in drug development and in academic research. The use of PBPK models is continuously expanding, with the majority of work now focusing on predictions of drug-drug interactions, drug-disease interactions, and changes in drug disposition across lifespan. Recently, publications that use PBPK modeling to predict drug disposition during pregnancy and in organ impairment have increased reflecting the advances in incorporating diverse physiologic changes into the models. Because of the expanding computational power and diversity of modeling platforms available, the complexity of PBPK models has also increased. Academic efforts have provided clear advances in better capturing human physiology in PBPK models and incorporating more complex mathematical concepts into PBPK models. Examples of such advances include the segregated gut model with a series of gut compartments allowing modeling of physiologic blood flow distribution within an organ and zonation of metabolic enzymes and series compartment liver models allowing simulations of hepatic clearance for high extraction drugs. Despite these advances in academic research, the progress in assessing model quality and defining model acceptance criteria based on the intended use of the models has not kept pace. This Minireview suggests that awareness of the need for predefined criteria for model acceptance has increased, but many manuscripts still lack description of scientific justification and/or rationale for chosen acceptance criteria. As artificial intelligence and machine learning approaches become more broadly accepted, these tools offer promise for development of comprehensive assessment for existing observed data and analysis of model performance. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) modeling has become a mainstream application in academic literature and is broadly used for predictions, analysis, and evaluation of pharmacokinetic data. Significant progress has been made in developing advanced PBPK models that better capture human physiology, but oftentimes sufficient justification for the chosen model acceptance criterion and model structure is still missing. This Minireview provides a summary of the current landscape of PBPK applications used and highlights the need for advancing PBPK modeling science and training in academia.

Model-informed drug discovery and development approaches to inform clinical trial design and regulatory decisions: A primer for the MENA region

Model-Informed Drug Discovery and Development (MID3) represents a transformative approach in pharmaceutical research, integrating quantitative models to inform and optimize decision-making throughout the drug development process. This review explores the current applications, challenges, and future prospects of MID3 within the Middle East and North Africa (MENA) region. By leveraging local data and advanced computational techniques, MID3 has the potential to significantly enhance the efficiency and success rates of drug development tailored to regional health priorities. We discussed successful case studies of applying MID3 at different phases of drug development and clinical trials. Furthermore, we emphasized the critical need for MENA countries to embrace MID3 by investing in workforce training, aligning regulatory frameworks, and fostering collaborative research initiatives. This call to action underscores the importance of a robust MID3 ecosystem, urging policymakers, academic institutions, and industry stakeholders to prioritize and support its integration into the MENA region's healthcare.

Physiologically-based pharmacokinetic modelling in sepsis: A tool to elucidate how pathophysiology affects meropenem pharmacokinetics

OBJECTIVES

Applying physiologically-based pharmacokinetic (PBPK) modelling in sepsis could help to better understand how PK changes are influenced by drug- and patient-related factors. We aimed to elucidate the influence of sepsis pathophysiology on the PK of meropenem by applying PBPK modelling.

METHODS

A whole-body meropenem PBPK model was developed and evaluated in healthy individuals, and renally impaired non-septic patients. Sepsis-induced physiological changes in body composition, organ blood flow, kidney function, albumin, and haematocrit were implemented according to a previously proposed PBPK sepsis model. Model performance was evaluated, and a local sensitivity analysis was conducted.

RESULTS

The model-predicted PK metrics (AUC, Cmax, CL, Vss) were within 1.33-fold-error margin of published data for 87.5% of the simulated profiles in healthy individuals. In sepsis, the model provided good predictions for literature-digitised average plasma and tissue exposure data, where the model-predicted AUC was within 1.33-fold-error margin for 9 out 11 simulated study profiles. Furthermore, the model was applied to individual plasma concentration data from 52 septic patients, where the model-predicted AUC, Cmax, and CL had a fold-error ratio range of 0.98-1.12, with alignment of the predicted and observed variability. For Vss, the fold-error ratio was 0.81, and the model underpredicted the population variability. CL was sensitive to renal plasma clearance, and kidney volume, whereas Vss was sensitive to the unbound fraction, organ volume fraction of the interstitial compartment, and the organ volume.

CONCLUSIONS

These findings may be extended to more diverse drug types and support a more mechanistic understanding of the effect of sepsis on drug exposure.

Model-Based Dose Selection of a Sphingosine-1-Phosphate Modulator, Etrasimod, in Patients with Various Degrees of Hepatic Impairment

BACKGROUND/OBJECTIVES

Etrasimod is a newly FDA-approved Sphingosine-1-Phosphate modulator indicated for moderate and severe ulcerative colitis. It is extensively metabolized in the liver via the cytochrome P450 system and may accumulate markedly in patients with hepatic dysfunction, exposing them to toxicity. The aim of the current study is to utilize a physiologically-based pharmacokinetic modeling approach to evaluate the impact of hepatic impairment on the pharmacokinetic behavior of etrasimod and to appropriately select dosage regimens for patients with chronic liver disease; Methods: PK-Sim was used to develop the etrasimod PBPK model, which was verified using clinical data from healthy subjects and subsequently adapted to reflect the physiological changes associated with varying degrees of hepatic dysfunction; Results: Simulations indicated that hepatic clearance of etrasimod is clearly reduced in patients with Child-Pugh B and C liver impairment. Based on these findings, dosing adjustments were proposed to achieve therapeutic exposures equivalent to those in individuals with normal liver function. In the Child-Pugh B and C population groups, 75% and 62.5%, respectively, of the standard dose were enough to have comparable exposure to the healthy population. These adjusted dosages aim to mitigate the risk of drug toxicity while maintaining efficacy; Conclusions: The PBPK model provides a robust framework for individualizing drug therapy in patients with hepatic impairment, ensuring safer and more effective treatment outcomes. Further clinical studies are warranted to verify these dosing recommendations and to refine the model for broader clinical applications.

Advancements in Virtual Bioequivalence: A Systematic Review of Computational Methods and Regulatory Perspectives in the Pharmaceutical Industry

BACKGROUND/OBJECTIVES

The rise of virtual bioequivalence studies has transformed the pharmaceutical landscape, enabling more efficient drug development processes. This systematic review aims to explore advancements in physiologically based pharmacokinetic (PBPK) modeling, its regulatory implications, and its role in achieving virtual bioequivalence, particularly for complex drug formulations.

METHODS

We conducted a systematic review of clinical trials using computational methods, particularly PBPK modeling, to carry out bioequivalence assessments. Eligibility criteria are emphasized during in silico modeling and pharmacokinetic simulations. Comprehensive literature searches were performed across databases such as PubMed, Scopus, and the Cochrane Library. A search strategy using key terms and Boolean operators ensured that extensive coverage was achieved. We adhered to the PRISMA guidelines in regard to the study selection, data extraction, and quality assessment, focusing on key characteristics, methodologies, outcomes, and regulatory perspectives from the FDA and EMA.

RESULTS

Our findings indicate that PBPK modeling significantly enhances the prediction of pharmacokinetic profiles, optimizing dosing regimens, while minimizing the need for extensive clinical trials. Regulatory agencies have recognized this utility, with the FDA and EMA developing frameworks to integrate in silico methods into drug evaluations. However, challenges such as study heterogeneity and publication bias may limit the generalizability of the results.

CONCLUSIONS

This review highlights the critical need for standardized protocols and robust regulatory guidelines to facilitate the integration of virtual bioequivalence methodologies into pharmaceutical practices. By embracing these advancements, the pharmaceutical industry can improve drug development efficiency and patient outcomes, paving the way for innovative therapeutic solutions. Continued research and adaptive regulatory frameworks will be essential in navigating this evolving field.

Evaluation of Drug-Drug Interaction Potential of Talquetamab, a T-Cell-Redirecting GPRC5D × CD3 Bispecific Antibody, as a Result of Cytokine Release Syndrome in Patients with Relapsed/Refractory Multiple Myeloma in MonumenTAL-1, Using a Physiologically Based Pharmacokinetic Model

BACKGROUND

Cytokine release syndrome, commonly associated with T-cell immunotherapies, was observed with talquetamab, a T-cell-redirecting bispecific antibody, in the phase I/II MonumenTAL-1 study, leading to elevated interleukin (IL)-6, which can suppress cytochrome P450 (CYP) enzyme activity.

OBJECTIVE

We aimed to evaluate the potential impact of elevated IL-6 on the exposure of co-administered CYP450 substrates for two scenarios: (1) the observed median IL-6 profile and (2) a profile with the highest IL-6 maximum concentration following talquetamab treatment.

METHODS

A physiologically based pharmacokinetic model was developed based on the literature and simulations performed using observed IL-6 profiles from patients in MonumenTAL-1 who received the subcutaneous recommended phase 2 doses (RP2Ds) of talquetamab: 0.4 mg/kg weekly (QW) and 0.8 mg/kg every other week (Q2W).

RESULTS

Median IL-6 maximum concentration was 18.4 and 7.1 pg/mL, and maximum IL-6 maximum concentration was 213 and 3503 pg/mL for talquetamab QW and Q2W RP2Ds, respectively. For the median IL-6 profile, no interaction between IL-6 and studied CYP substrates was predicted at either RP2D. The maximum IL-6 profile predicted weak-to-moderate impact on exposure of CYP2C19, CYP3A4, and CYP3A5 substrates and minimal impact on exposure of CYP1A2 substrates at both RP2Ds. Impact on exposure of CYP2C9 substrates was predicted as minimal at QW and minimal-to-weak at Q2W RP2Ds. Time to return to 20% difference from baseline enzymatic activity was predicted as 7 and 9 days after start of cycle 1 for QW and Q2W RP2Ds, respectively.

CONCLUSIONS

These modeling results suggest that IL-6 release due to talquetamab-induced cytokine release syndrome has limited impact on potential drug-drug interactions, with the highest likelihood of impact occurring from initiation of talquetamab step-up dosing up to 7 (QW) or 9 (Q2W) days after first treatment dose in cycle 1 and during and after cytokine release syndrome. Multiple myeloma can be treated with immunotherapies such as the bispecific antibody, talquetamab, which binds the novel antigen G protein-coupled receptor family C group 5 member D on multiple myeloma cells and CD3 on T cells and induces T-cell-mediated lysis of multiple myeloma cells. Following talquetamab treatment, many patients experience cytokine release syndrome, an inflammatory immune response where levels of proinflammatory cytokines, including interleukin (IL)-6, are increased. Interleukin-6 can suppress the activity of important enzymes in the body (cytochrome [CYP] P450s) that are involved in drug clearance. This study used a physiologically based pharmacokinetic computer model to investigate the potential impact of increased IL-6 levels on CYP450 enzymes to determine subsequent impact on drugs that are metabolized by CYP450 enzymes. The results showed no predicted interaction between median levels of IL-6 observed in patients and CYP substrates (such as caffeine and omeprazole) with talquetamab. In a simulation that assessed higher (maximum) IL-6 levels observed in patients, the predicted impact of IL-6 was minimal to weak for most of the CYP substrates assessed. The effect on CYP450 enzymatic activity was highest from initiation of talquetamab step-up dosing up to 7-9 days after the first treatment dose of talquetamab. These results suggest that, in this treatment time period, elevated IL-6 levels due to talquetamab-induced cytokine release syndrome have limited impact on drugs that are CYP substrates that may be used in conjunction with talquetamab, but that the concentration and toxicity of these drugs should be monitored and the dose of CYP substrate adjusted as required.

Physiologically Based Pharmacokinetic Modeling to Assess Ritonavir-Digoxin Interactions and Recommendations for Co-Administration Regimens

BACKGROUND

Digoxin is a commonly used cardiac glycoside drug in clinical practice, primarily transported by P-glycoprotein (P-gp) and susceptible to the influence of P-gp inhibitors/inducers. Concurrent administration of ritonavir and digoxin may significantly increase the plasma concentration of digoxin. Due to the narrow therapeutic window of digoxin, combined use may lead to severe toxic effects.

PURPOSE

Utilize a Physiology-Based Pharmacokinetic (PBPK) model to simulate and predict the impact of the interaction between ritonavir and digoxin on the pharmacokinetics (PK) of digoxin, and provide recommendations for the combined medication regimen.

METHODS

Using PK-Sim®, develop individual PBPK models for ritonavir and digoxin. Simulate the exposure in a drug-drug interaction (DDI) scenario by implementing ritonavir's inhibition of P-glycoprotein (P-gp) on digoxin. Evaluate the performance of the models by comparing the predicted and observed plasma concentration-time curves and predicted versus observed PK parameter values. Finally, adjust the dosing regimen for the combined therapy based on the changes in exposure.

RESULTS

According to the model simulations, the steady-state exposure of digoxin increased by 86.5% and 90.2% for oral administration, and 80.2% and 90.2% for intravenous administration, respectively, when 0.25 mg or 0.5 mg of digoxin was administered concurrently with ritonavir. By reducing the dose of digoxin by 45% or doubling the oral administration interval, similar steady-state concentrations can be achieved compared to when the drugs are not co-administered.

CONCLUSIONS

In clinical practice, the influence of drug interactions on the plasma concentration changes of digoxin within the body should be considered to ensure the safety and effectiveness of treatment.

Advancements in physiologically based pharmacokinetic modeling for fedratinib: updating dose guidance in the presence of a dual inhibitor of CYP3A4 and CYP2C19

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for fedratinib was updated and revalidated to bridge a gap between the observed drug-drug interaction (DDI) of a single sub-efficacious dose in healthy participants and the potential DDI in patients with cancer at steady state. The study aimed to establish an appropriate dose for fedratinib in patients coadministered with dual CYP3A4 and CYP2C19 inhibitors, providing quantitative evidence to inform dosing guidance.

METHODS

The original minimal PBPK model was developed using Simcyp® Simulator v17. The model was updated by substituting a single distribution rate (Qsac) with 2 separate rates (CLin/CLout) and transitioning to v20. Model parameter updates were further informed with 3 clinical studies, and 3 more studies served as independent validation data. The validated model was applied to simulate potential DDIs between fedratinib and a known dual inhibitor of CYP3A4 and CYP2C19 (fluconazole).

RESULTS

Coadministration of fedratinib with fluconazole in patients was predicted to increase fedratinib exposure by < 2-fold in all simulated scenarios. For patients with cancer receiving the approved dose of fedratinib 400 mg once daily along with fluconazole 200 mg daily, the model predicted an approximate 50% increase in fedratinib exposure at steady state.

CONCLUSIONS

The updated PBPK model improved description of the observed pharmacokinetics and predicted a low risk of clinically significant DDIs between fedratinib and fluconazole. The quantitative evidence serves as a primary foundation for providing dose guidance in clinical practice for the coadministration of fedratinib with dual CYP3A4 and CYP2C19 inhibitors.

Application of Mechanistic Multiparameter Optimization and Large-Scale In Vitro to In Vivo Pharmacokinetics Correlations to Small-Molecule Therapeutic Projects

Computational chemistry and machine learning are used in drug discovery to predict the target-specific and pharmacokinetic properties of molecules. Multiparameter optimization (MPO) functions are used to summarize multiple properties into a single score, aiding compound prioritization. However, over-reliance on subjective MPO functions risks reinforcing human bias. Mechanistic modeling approaches based on physiological relevance can be adapted to meet different potential key objectives of the project (e.g., minimizing dose, maximizing safety margins, and/or minimizing drug-drug interaction risk) while retaining the same underlying model structure. The current work incorporates recent approaches to predict in vivo pharmacokinetic (PK) properties and validates in vitro to in vivo correlation analysis to support mechanistic PK MPO. Examples of use and impact in small-molecule drug discovery projects are provided. Overall, the mechanistic MPO identifies 83% of the compounds considered as short-listed for clinical experiments in the top second percentile, and 100% in the top 10th percentile, resulting in an area under the receiver operating characteristic curve (AUCROC) > 0.95. In addition, the MPO score successfully recapitulates the chronological progression of the optimization process across different scaffolds. Finally, the MPO scores for compounds characterized in pharmacokinetics experiments are markedly higher compared with the rest of the compounds being synthesized, highlighting the potential of this tool to reduce the reliance on in vivo testing for compound screening.

Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration

Physiologically based pharmacokinetic (PBPK) models which can leverage preclinical data to predict the pharmacokinetic properties of drugs rapidly became an essential tool to improve the efficiency and quality of novel drug development. In this review, by searching the Application Review Files in Drugs@FDA, we analyzed the current application of PBPK models in novel drugs approved by the U.S. Food and Drug Administration (FDA) in the past five years. According to the results, 243 novel drugs were approved by the FDA from 2019 to 2023. During this period, 74 Application Review Files of novel drugs approved by the FDA that used PBPK models. PBPK models were used in various areas, including drug-drug interactions (DDI), organ impairment (OI) patients, pediatrics, drug-gene interaction (DGI), disease impact, and food effects. DDI was the most widely used area of PBPK models for novel drugs, accounting for 74.2 % of the total. Software platforms with graphical user interfaces (GUI) have reduced the difficulty of PBPK modeling, and Simcyp was the most popular software platform among applicants, with a usage rate of 80.5 %. Despite its challenges, PBPK has demonstrated its potential in novel drug development, and a growing number of successful cases provide experience learned for researchers in the industry.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Physiologically based pharmacokinetic modeling in obesity: applications and challenges

INTRODUCTION

Rising global obesity rates pose a threat to people's health. Obesity causes a series of pathophysiologic changes, making the response of patients with obesity to drugs different from that of nonobese, thus affecting the treatment efficacy and even leading to adverse events. Therefore, understanding obesity's effects on pharmacokinetics is essential for the rational use of drugs in patients with obesity.

AREAS COVERED

Articles related to physiologically based pharmacokinetic (PBPK) modeling in patients with obesity from inception to October 2023 were searched in PubMed, Embase, Web of Science and the Cochrane Library. This review outlines PBPK modeling applications in exploring factors influencing obesity's effects on pharmacokinetics, guiding clinical drug development and evaluating and optimizing clinical use of drugs in patients with obesity.

EXPERT OPINION

Obesity-induced pathophysiologic alterations impact drug pharmacokinetics and drug-drug interactions (DDIs), altering drug exposure. However, there is a lack of universal body size indices or quantitative pharmacology models to predict the optimal for the patients with obesity. Therefore, dosage regimens for patients with obesity must consider individual physiological and biochemical information, and clinically individualize therapeutic drug monitoring for highly variable drugs to ensure effective drug dosing and avoid adverse effects.

Physiologically based pharmacokinetic modeling of CYP2C8 substrate rosiglitazone and its metabolite to predict metabolic drug-drug interaction

Rosiglitazone is an activator of nuclear peroxisome proliferator-activated (PPAR) receptor gamma used in the treatment of type 2 diabetes mellitus. The elimination of rosiglitazone occurs mainly via metabolism, with major contribution by enzyme cytochrome P450 (CYP) 2C8. Primary routes of rosiglitazone metabolism are N-demethylation and hydroxylation. Modulation of CYP2C8 activity by co-administered drugs lead to prominent changes in the exposure of rosiglitazone and its metabolites. Here, we attempt to develop mechanistic parent-metabolite physiologically based pharmacokinetic (PBPK) model for rosiglitazone. Our goal is to predict potential drug-drug interaction (DDI) and consequent changes in metabolite N-desmethyl rosiglitazone exposure. The PBPK modeling was performed in the PKSim® software using clinical pharmacokinetics data from literature. The contribution to N-desmethyl rosiglitazone formation by CYP2C8 was delineated using vitro metabolite formation rates from recombinant enzyme system. Developed model was verified for prediction of rosiglitazone DDI potential and its metabolite exposure based on observed clinical DDI studies. Developed model exhibited good predictive performance both for rosiglitazone and N-desmethyl rosiglitazone respectively, evaluated based on commonly acceptable criteria. In conclusion, developed model helps with prediction of CYP2C8 DDI using rosiglitazone as a substrate, as well as changes in metabolite exposure. In vitro data for metabolite formation can be successfully utilized to translate to in vivo conditions.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

Prediction method of pharmacokinetic parameters of small molecule drugs based on GCN network model

CONTEXT

Accurately predicting plasma protein binding rate (PPBR) and oral bioavailability (OBA) helps to better reveal the absorption and distribution of drugs in the human body and subsequent drug design. Although machine learning models have achieved good results in prediction accuracy, they often suffer from insufficient accuracy when dealing with data with irregular topological structures.

METHODS

In view of this, this study proposes a pharmacokinetic parameter prediction framework based on graph convolutional networks (GCN), which predicts the PPBR and OBA of small molecule drugs. In the framework, GCN is first used to extract spatial feature information on the topological structure of drug molecules, in order to better learn node features and association information between nodes. Then, based on the principle of drug similarity, this study calculates the similarity between small molecule drugs, selects different thresholds to construct datasets, and establishes a prediction model centered on the GCN algorithm. The experimental results show that compared with traditional machine learning prediction models, the prediction model constructed based on the GCN method performs best on PPBR and OBA datasets with an inter-molecular similarity threshold of 0.25, with MAE of 0.155 and 0.167, respectively. In addition, in order to further improve the accuracy of the prediction model, GCN is combined with other algorithms. Compared to using a single GCN method, the distribution of the predicted values obtained by the combined model is highly consistent with the true values. In summary, this work provides a new method for improving the rate of early drug screening in the future.

Applying quantitative and systems pharmacology to drug development and beyond: An introduction to clinical pharmacologists

ABSTRACT

Quantitative and systems pharmacology (QSP) is an innovative and integrative approach combining physiology and pharmacology to accelerate medical research. This review focuses on QSP's pivotal role in drug development and its broader applications, introducing clinical pharmacologists/researchers to QSP's quantitative approach and the potential to enhance their practice and decision-making. The history of QSP adoption reveals its impact in diverse areas, including glucose regulation, oncology, autoimmune disease, and HIV treatment. By considering receptor-ligand interactions of various cell types, metabolic pathways, signaling networks, and disease biomarkers simultaneously, QSP provides a holistic understanding of interactions between the human body, diseases, and drugs. Integrating knowledge across multiple time and space scales enhances versatility, enabling insights into personalized responses and general trends. QSP consolidates vast data into robust mathematical models, predicting clinical trial outcomes and optimizing dosing based on preclinical data. QSP operates under a "learn and confirm paradigm," integrating experimental findings to generate testable hypotheses and refine them through precise experimental designs. An interdisciplinary collaboration involving expertise in pharmacology, biochemistry, genetics, mathematics, and medicine is vital. QSP's utility in drug development is demonstrated through integration in various stages, predicting drug responses, optimizing dosing, and evaluating combination therapies. Challenges exist in model complexity, communication, and peer review. Standardized workflows and evaluation methods ensure reliability and transparency.

Evaluating drug interaction potential from cytokine release syndrome using a physiologically based pharmacokinetic model: A case study of teclistamab

Cytokine release syndrome (CRS) was associated with teclistamab treatment in the phase I/II MajesTEC-1 study. Cytokines, especially interleukin (IL)-6, are known suppressors of cytochrome P450 (CYP) enzymes' activity. A physiologically based pharmacokinetic model evaluated the impact of IL-6 serum levels on exposure of substrates of various CYP enzymes (1A2, 2C9, 2C19, 3A4, 3A5). Two IL-6 kinetics profiles were assessed, the mean IL-6 profile with a maximum concentration (Cmax) of IL-6 (21 pg/mL) and the IL-6 profile of the patient presenting the highest IL-6 Cmax (288 pg/mL) among patients receiving the recommended phase II dose of teclistamab in MajesTEC-1. For the mean IL-6 kinetics profile, teclistamab was predicted to result in a limited change in exposure of CYP substrates (area under the curve [AUC] mean ratio 0.87-1.20). For the maximum IL-6 kinetics profile, the impact on omeprazole, simvastatin, midazolam, and cyclosporine exposure was weak to moderate (mean AUC ratios 1.90-2.23), and minimal for caffeine and s-warfarin (mean AUC ratios 0.82-1.25). Maximum change in exposure for these substrates occurred 3-4 days after step-up dosing in cycle 1. These results suggest that after cycle 1, drug interaction from IL-6 effect has no meaningful impact on CYP activities, with minimal or moderate impact on CYP substrates. The highest risk of drug interaction is expected to occur during step-up dosing up to 7 days after the first treatment dose (1.5 mg/kg subcutaneously) and during and after CRS.

Recent Progress on Physiologically Based Pharmacokinetic (PBPK) Model: A Review Based on Bibliometrics

Physiologically based pharmacokinetic/toxicokinetic (PBPK/PBTK) models are designed to elucidate the mechanism of chemical compound action in organisms based on the physiological, biochemical, anatomical, and thermodynamic properties of organisms. After nearly a century of research and practice, good results have been achieved in the fields of medicine, environmental science, and ecology. However, there is currently a lack of a more systematic review of progress in the main research directions of PBPK models, especially a more comprehensive understanding of the application in aquatic environmental research. In this review, a total of 3974 articles related to PBPK models from 1996 to 24 March 2024 were collected. Then, the main research areas of the PBPK model were categorized based on the keyword co-occurrence maps and cluster maps obtained by CiteSpace. The results showed that research related to medicine is the main application area of PBPK. Four major research directions included in the medical field were "drug assessment", "cross-species prediction", "drug-drug interactions", and "pediatrics and pregnancy drug development", in which "drug assessment" accounted for 55% of the total publication volume. In addition, bibliometric analyses indicated a rapid growth trend in the application in the field of environmental research, especially in predicting the residual levels in organisms and revealing the relationship between internal and external exposure. Despite facing the limitation of insufficient species-specific parameters, the PBPK model is still an effective tool for improving the understanding of chemical-biological effectiveness and will provide a theoretical basis for accurately assessing potential risks to ecosystems and human health. The combination with the quantitative structure-activity relationship model, Bayesian method, and machine learning technology are potential solutions to the previous research gaps.

Physiologically based modeling of LNP-mediated delivery of mRNA in the vascular system

RNA therapeutics are an emerging, powerful class of drugs with potential applications in a wide range of disorders. A central challenge in their development is the lack of clear pharmacokinetic (PK)-pharmacodynamic relationship, in part due to the significant delay between the kinetics of RNA delivery and the onset of pharmacologic response. To bridge this gap, we have developed a physiologically based PK/pharmacodynamic model for systemically administered mRNA-containing lipid nanoparticles (LNPs) in mice. This model accounts for the physiologic determinants of mRNA delivery, active targeting in the vasculature, and differential transgene expression based on nanoparticle coating. The model was able to well-characterize the blood and tissue PKs of LNPs, as well as the kinetics of tissue luciferase expression measured by ex vivo activity in organ homogenates and bioluminescence imaging in intact organs. The predictive capabilities of the model were validated using a formulation targeted to intercellular adhesion molecule-1 and the model predicted nanoparticle delivery and luciferase expression within a 2-fold error for all organs. This modeling platform represents an initial strategy that can be expanded upon and utilized to predict the in vivo behavior of RNA-containing LNPs developed for an array of conditions and across species.

Abemaciclib pharmacology and interactions in the treatment of HR+/HER2- breast cancer: a critical review

Abemaciclib (ABE) in combination with endocrine therapy represents the mainstay treatment for either endocrine-resistant metastatic or high-risk early-stage HR+/HER2- breast cancer patients. Hence, an adequate knowledge of this agent pharmacodynamic, pharmacokinetic, and of its drug-drug interactions (DDIs) is crucial for an optimal patients management. Additionally, ABE interference with food and complementary/alternative medicines should be taken into account in the clinical practice. Several online tools allow to freely check DDIs and can be easily consulted before prescribing ABE. According to one of this instruments, ABE display the lowest number of interactions among the available cyclin-dependent kinase 4/6 inhibitors. Still, clinicians should be aware that online tools cannot replace the technical datasheet of the drug as well as a comprehensive clinical assessment for each patient. Here we critically review the main pharmacological features of ABE, then focusing on its potential interactions with drugs, food, and alternative medicine, in order to provide a guide for its optimal use in the treatment of HR+/HER2- breast cancer patients.

The Effect of Inflammatory Bowel Disease and Irritable Bowel Syndrome on Pravastatin Oral Bioavailability: In vivo and in silico evaluation using bottom-up wbPBPK modeling

The common disorders irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) can modify the drugs' pharmacokinetics via their induced pathophysiological changes. This work aimed to investigate the impact of these two diseases on pravastatin oral bioavailability. Rat models for IBS and IBD were used to experimentally test the effects of IBS and IBD on pravastatin pharmacokinetics. Then, the observations made in rats were extrapolated to humans using a mechanistic whole-body physiologically-based pharmacokinetic (wbPBPK) model. The rat in vivo studies done herein showed that IBS and IBD decreased serum albumin (> 11% for both), decreased PRV binding in plasma, and increased pravastatin absolute oral bioavailability (0.17 and 0.53 compared to 0.01) which increased plasma, muscle, and liver exposure. However, the wbPBPK model predicted muscle concentration was much lower than the pravastatin toxicity thresholds for myotoxicity and rhabdomyolysis. Overall, IBS and IBD can significantly increase pravastatin oral bioavailability which can be due to a combination of increased pravastatin intestinal permeability and decreased pravastatin gastric degradation resulting in higher exposure. This is the first study in the literature investigating the effects of IBS and IBD on pravastatin pharmacokinetics. The high interpatient variability in pravastatin concentrations as induced by IBD and IBS can be reduced by oral administration of pravastatin using enteric-coated tablets. Such disease (IBS and IBD)-drug interaction can have more drastic consequences for narrow therapeutic index drugs prone to gastric degradation, especially for drugs with low intestinal permeability.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

BACKGROUND

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

METHODS

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values.

RESULTS

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

CONCLUSIONS

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Predicting drug-drug interactions in breast cancer patients treated with CDK4/6 inhibitors and forward planning

INTRODUCTION

Cyclin-dependent kinase (CDK) 4/6 inhibitors are cornerstones in the treatment of Hormone Receptor (HR) positive and Human Epidermal Growth factor (HER2) negative metastatic breast cancer. Given their widespread use in the metastatic setting and emerging use in the adjuvant setting, studying drug-drug interactions (DDI) of these medications is of utmost importance.

AREAS COVERED

This review provides key background information on the CDK4/6 inhibitors, palbociclib, ribociclib, and abemaciclib. We discuss drug-drug interactions including those with proton pump inhibitors as well as CYP3A substrates, inhibitors, and inducers. We describe the effect of these drugs on membrane transporters and their substrates as well as those drugs that increase risk of CDK4/6 toxicities. Finally, we explore future directions for strategies to minimize drug-drug interactions.

EXPERT OPINION

It is crucial to be mindful of medications that may interfere with drug absorption, such as proton pump inhibitors, as well as those that interfere with drug metabolism, such as CYP3A4 inhibitors and inducers. Additionally, special consideration should be given to populations at higher risk for polypharmacy, such as older patients with greater comorbidities. These interactions and patient characteristics should be considered when developing individual treatment plans with CDK4/6 inhibitors.

Challenging the status quo: a framework for mechanistic and human-relevant cardiovascular safety screening

Traditional approaches to preclinical drug safety assessment have generally protected human patients from unintended adverse effects. However, these assessments typically occur too late to make changes in the formulation or in phase 1 and beyond, are highly dependent on animal studies and have the potential to lead to the termination of useful drugs due to liabilities in animals that are not applicable in patients. Collectively, these elements come at great detriment to both patients and the drug development sector. This phenomenon is particularly problematic in the area of cardiovascular safety assessment where preclinical attrition is high. We believe that a more efficient and translational approach can be defined. A multi-tiered assessment that leverages our understanding of human cardiovascular biology, applies human cell-based in vitro characterizations of cardiovascular responses to insult, and incorporates computational models of pharmacokinetic relationships would enable earlier and more translational identification of human-relevant liabilities. While this will take time to develop, the ultimate goal would be to implement such assays both in the lead selection phase as well as through regulatory phases.

Physiologically Based Pharmacokinetic Modeling of Lacosamide in Patients With Hepatic and Renal Impairment and Pediatric Populations to Support Pediatric Dosing Optimization

PURPOSE

Lacosamide (LCM) is a new-generation anti-seizure medication that is efficacious in patients with focal seizures with or without secondary generalization. Until now, the efficacy, safety, and tolerability of LCM are still lacking in Chinese epilepsy patients, particularly for pediatric populations and patients with renal or hepatic impairment.

METHODS

This study was conducted to develop a physiologically based pharmacokinetic (PBPK) model to characterize the pharmacokinetics of LCM in Chinese populations and predict the pharmacokinetics of LCM in Chinese pediatric populations and patients with renal or hepatic impairment. Using data from clinical investigations, the developed PBPK model was validated by comparing predicted and observed blood concentration data.

FINDINGS

Doses should be reduced to approximately 82%, 75%, 63%, and 76% of the Chinese healthy adult dose in patients with mild, moderate, and severe renal impairment and end-stage renal disease; and approximately 89%, 72%, and 36% of the Chinese healthy adult dose in patients with Child Pugh-A, B, and C hepatic impairment. For pediatric populations, intravenous doses should be adjusted to 1.75 mg/kg for newborns, 2.5 mg/kg for toddlers, 2.2 mg/kg mg for preschool and school age, and 2 mg/kg mg for adolescents to achieve an equivalent plasma exposure of 2 mg/kg LCM in adults. The oral doses should be adjusted to 20 mg for toddlers, 32 mg for preschool, 45 mg for school age, and 95 mg for adolescents to achieve an approximately equivalent plasma exposure of 100 mg LCM in adults.

IMPLICATIONS

The PBPK model of LCM can be utilized to optimize dosage regimens for special populations.

Interspecies Brain PBPK Modeling Platform to Predict Passive Transport through the Blood-Brain Barrier and Assess Target Site Disposition

The high failure rate of central nervous system (CNS) drugs is partly associated with an insufficient understanding of target site exposure. Blood-brain barrier (BBB) permeability evaluation tools are needed to explore drugs' ability to access the CNS. An outstanding aspect of physiologically based pharmacokinetic (PBPK) models is the integration of knowledge on drug-specific and system-specific characteristics, allowing the identification of the relevant factors involved in target site distribution. We aimed to qualify a PBPK platform model to be used as a tool to predict CNS concentrations when significant transporter activity is absent and human data are sparse or unavailable. Data from the literature on the plasma and CNS of rats and humans regarding acetaminophen, oxycodone, lacosamide, ibuprofen, and levetiracetam were collected. Human BBB permeability values were extrapolated from rats using inter-species differences in BBB surface area. The percentage of predicted AUC and Cmax within the 1.25-fold criterion was 85% and 100% for rats and humans, respectively, with an overall GMFE of <1.25 in all cases. This work demonstrated the successful application of the PBPK platform for predicting human CNS concentrations of drugs passively crossing the BBB. Future applications include the selection of promising CNS drug candidates and the evaluation of new posologies for existing drugs.

A deep neural network: mechanistic hybrid model to predict pharmacokinetics in rat

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893-4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.

Circulating Biomarkers Instead of Genotyping to Establish Metabolizer Phenotypes

Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.

Baricitinib and tofacitinib off-target profile, with a focus on Alzheimer's disease

INTRODUCTION

Janus kinase (JAK) inhibitors were recently identified as promising drug candidates for repurposing in Alzheimer's disease (AD) due to their capacity to suppress inflammation via modulation of JAK/STAT signaling pathways. Besides interaction with primary therapeutic targets, JAK inhibitor drugs frequently interact with unintended, often unknown, biological off-targets, leading to associated effects. Nevertheless, the relevance of JAK inhibitors' off-target interactions in the context of AD remains unclear.

METHODS

Putative off-targets of baricitinib and tofacitinib were predicted using a machine learning (ML) approach. After screening scientific literature, off-targets were filtered based on their relevance to AD. Targets that had not been previously identified as off-targets of baricitinib or tofacitinib were subsequently tested using biochemical or cell-based assays. From those, active concentrations were compared to bioavailable concentrations in the brain predicted by physiologically based pharmacokinetic (PBPK) modeling.

RESULTS

With the aid of ML and in vitro activity assays, we identified two enzymes previously unknown to be inhibited by baricitinib, namely casein kinase 2 subunit alpha 2 (CK2-α2) and dual leucine zipper kinase (MAP3K12), both with binding constant (K d) values of 5.8 μM. Predicted maximum concentrations of baricitinib in brain tissue using PBPK modeling range from 1.3 to 23 nM, which is two to three orders of magnitude below the corresponding binding constant.

CONCLUSION

In this study, we extended the list of baricitinib off-targets that are potentially relevant for AD progression and predicted drug distribution in the brain. The results suggest a low likelihood of successful repurposing in AD due to low brain permeability, even at the maximum recommended daily dose. While additional research is needed to evaluate the potential impact of the off-target interaction on AD, the combined approach of ML-based target prediction, in vitro confirmation, and PBPK modeling may help prioritize drugs with a high likelihood of being effectively repurposed for AD.

Highlights

This study explored JAK inhibitors' off-targets in AD using a multidisciplinary approach.We combined machine learning, in vitro tests, and PBPK modelling to predict and validate new off-target interactions of tofacitinib and baricitinib in AD.Previously unknown inhibition of two enzymes (CK2-a2 and MAP3K12) by baricitinib were confirmed using in vitro experiments.Our PBPK model indicates that baricitinib low brain permeability limits AD repurposing.The proposed multidisciplinary approach optimizes drug repurposing efforts in AD research.

The Interplay of Permeability, Metabolism, Transporters, and Dosing in Determining the Dynamics of the Tissue/Plasma Partition Coefficient and Volume of Distribution-A Theoretical Investigation Using Permeability-Limited, Physiologically Based Pharmacokinetic Modeling

A permeability-limited physiologically based pharmacokinetic (PBPK) model featuring four subcompartments (corresponding to the intracellular and extracellular water of the tissue, the residual plasma, and blood cells) for each tissue has been developed in MATLAB/SimBiology and applied to various what-if scenario simulations. This model allowed us to explore the complex interplay of passive permeability, metabolism in tissue or residual blood, active uptake or efflux transporters, and different dosing routes (intravenous (IV) or oral (PO)) in determining the dynamics of the tissue/plasma partition coefficient (Kp) and volume of distribution (Vd) within a realistic pseudo-steady state. Based on the modeling exercise, the permeability, metabolism, and transporters demonstrated significant effects on the dynamics of the Kp and Vd for IV bolus administration and PO fast absorption, but these effects were not as pronounced for IV infusion or PO slow absorption. Especially for low-permeability compounds, uptake transporters were found to increase both the Kp and Vd at the pseudo-steady state (Vdss), while efflux transporters had the opposite effect of decreasing the Kp and Vdss. For IV bolus administration and PO fast absorption, increasing tissue metabolism was predicted to elevate the Kp and Vdss, which contrasted with the traditional derivation from the steady-state perfusion-limited PBPK model. Moreover, metabolism in the residual blood had more impact on the Kp and Vdss compared to metabolism in tissue. Due to its ability to offer a more realistic description of tissue dynamics, the permeability-limited PBPK model is expected to gain broader acceptance in describing clinical PK and observed Kp and Vdss, even for certain small molecules like cyclosporine, which are currently treated as perfusion-limited in commercial PBPK platforms.

ADME characterization and PBK model development of 3 highly protein-bound UV filters through topical application

Estimating human exposure in the safety assessment of chemicals is crucial. Physiologically based kinetic (PBK) models which combine information on exposure, physiology, and chemical properties, describing the absorption, distribution, metabolism, and excretion (ADME) processes of a chemical, can be used to calculate internal exposure metrics such as maximum concentration and area under the concentration-time curve in plasma or tissues of a test chemical in next-generation risk assessment. This article demonstrates the development of PBK models for 3 UV filters, specifically octyl methoxycinnamate, octocrylene, and 4-methylbenzylidene camphor. The models were parameterized entirely based on data obtained from in vitro and/or in silico methods in a bottom-up modeling approach and then validated based on human dermal pharmacokinetic (PK) data. The 3 UV filters are "difficult to test" in in vitro test systems due to high lipophilicity, high binding affinity for proteins, and nonspecific binding, for example, toward plastic. This research work presents critical considerations in ADME data generation, interpretation, and parameterization to assure valid PBK model development to increase confidence in using PBK modeling to help make safety decisions in the absence of human PK data. The developed PBK models of the 3 chemicals successfully simulated the plasma concentration profiles of clinical PK data following dermal application, indicating the reliability of the ADME data generated and the parameters determined. The study also provides insights and lessons learned for characterizing ADME and developing PBK models for highly lipophilic and protein-bound chemicals in the future.

Physiologically Based Pharmacokinetic Modeling and Simulation of Mavacamten Exposure with Drug-Drug Interactions from CYP Inducers and Inhibitors by CYP2C19 Phenotype

Mavacamten is a first-in-class, oral, selective, allosteric, reversible cardiac myosin inhibitor approved by the US Food and Drug Administration for the treatment of adults with symptomatic New York Heart Association functional class II-III obstructive hypertrophic cardiomyopathy. Mavacamten is metabolized in the liver, predominantly via cytochrome P450 (CYP) enzymes CYP2C19 (74%), CYP3A4 (18%), and CYP2C9 (8%). A physiologically-based pharmacokinetic (PBPK) model was developed using Simcyp version 19 (Certara, Princeton, NJ). Following model verification, the PBPK model was used to explore the effects of strong CYP3A4 and CYP2C19 inducers, and strong, moderate, and weak CYP2C19 and CYP3A4 inhibitors on mavacamten pharmacokinetics (PK) in a healthy population, with the effect of CYP2C19 phenotype predicted for poor, intermediate, normal, and ultrarapid metabolizers. The PBPK model met the acceptance criteria for all verification simulations (> 80% of model-predicted PK parameters within 2-fold of those observed clinically). A weak induction effect was predicted when mavacamten was administered with a strong CYP3A4 inducer in poor metabolizers. Moderate reductions in mavacamten exposure were predicted with a strong CYP2C19/CYP3A4 inducer in all CYP2C19 phenotypes. Except for the effect of strong CYP2C19 inhibitors on ultrarapid metabolizers, steady-state area under plasma concentration-time curve and maximum plasma concentration values were weakly affected (< 2-fold) or not affected (< 1.25-fold), regardless of CYP2C19 phenotype. In conclusion, a fit-for-purpose PBPK model was developed and verified, which accurately predicted the available clinical data and was used to simulate the potential impact of CYP induction and inhibition on mavacamten PKs, stratified by CYP2C19 phenotype.

3D printing combined with biopredictive dissolution and PBPK/PD modeling optimization and personalization of pharmacotherapy: Are we there yet?

Precision medicine requires selecting the appropriate dosage regimen for a patient using the right drug, at the right time. Model-Informed Precision Dosing (MIPD) is a concept suggesting utilization of model-based prediction methods for optimizing the treatment benefit-harm balance, based on individual characteristics of the patient, disease, treatment method, and other factors. Here, we discuss a theoretical workflow comprising several elements, beginning from the physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models, through 3D printed tablets with the model proposed dose, information range and flow, and the patient themselves. We also describe each of these elements, and the connection between them, highlighting challenges and potential obstacles.

Shared learning from a physiologically based pharmacokinetic modeling strategy for human pharmacokinetics prediction through retrospective analysis of Genentech compounds

The quantitative prediction of human pharmacokinetics (PK) including the PK profile and key PK parameters are critical for early drug development decisions, successful phase I clinical trials, and the establishment of a range of doses to enable phase II clinical dose selection. Here, we describe an approach employing physiologically based pharmacokinetic (PBPK) modeling (Simcyp) to predict human PK and to validate its performance through retrospective analysis of 18 Genentech compounds for which clinical data are available. In short, physicochemical parameters and in vitro data for preclinical species were integrated using PBPK modeling to predict the in vivo PK observed in mouse, rat, dog, and cynomolgus monkey. Through this process, the in vitro to in vivo extrapolation (IVIVE) was determined and then incorporated into PBPK modeling in order to predict human PK. Overall, the prediction obtained using this PBPK-IVIVE approach captured the observed human PK profiles of the compounds from the dataset well. The predicted Cmax was within 2-fold of the observed Cmax for 94% of the compounds while the predicted area under the curve (AUC) was within 2-fold of the observed AUC for 72% of the compounds. Additionally, important IVIVE trends were revealed through this investigation, including application of scaling factors determined from preclinical IVIVE to human PK prediction for each molecule. Based upon the analysis, this PBPK-based approach now serves as a practical strategy for human PK prediction at the candidate selection stage at Genentech.

Physiologically based pharmacokinetic modeling of levetiracetam to predict the exposure in hepatic and renal impairment and elderly populations

Levetiracetam (LEV) is an anti-epileptic drug approved for use in various populations. The pharmacokinetic (PK) behavior of LEV may be altered in the elderly and patients with renal and hepatic impairment. Thus, dosage adjustment is required. This study was conducted to investigate how the physiologically-based PK (PBPK) model describes the PKs of LEV in adult and elderly populations, as well as to predict the PKs of LEV in patients with renal and hepatic impairment in both populations. The whole-body PBPK models were developed using the reported physicochemical properties of LEV and clinical data. The models were validated using data from clinical studies with different dose ranges and different routes and intervals of administration. The fit performance of the models was assessed by comparing predicted and observed blood concentration data and PK parameters. It is recommended that the doses be reduced to ~70%, 60%, and 45% of the adult dose for the mild, moderate, and severe renal impairment populations and ~95%, 80%, and 57% of the adult dose for the Child Pugh-A (CP-A), Child Pugh-B (CP-B), and Child Pugh-C (CP-C) hepatic impairment populations, respectively. No dose adjustment is required for the healthy elderly population, but dose reduction is required for the elderly with organ dysfunction accordingly, on a scale similar to that of adults. A PBPK model of LEV was successfully developed to optimize dosing regimens for special populations.

Development of a Minimalistic Physiologically Based Pharmacokinetic (mPBPK) Model for the Preclinical Development of Spectinamide Antibiotics

Spectinamides 1599 and 1810 are lead spectinamide compounds currently under preclinical development to treat multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis. These compounds have previously been tested at various combinations of dose level, dosing frequency, and route of administration in mouse models of Mycobacterium tuberculosis (Mtb) infection and in healthy animals. Physiologically based pharmacokinetic (PBPK) modeling allows the prediction of the pharmacokinetics of candidate drugs in organs/tissues of interest and extrapolation of their disposition across different species. Here, we have built, qualified, and refined a minimalistic PBPK model that can describe and predict the pharmacokinetics of spectinamides in various tissues, especially those relevant to Mtb infection. The model was expanded and qualified for multiple dose levels, dosing regimens, routes of administration, and various species. The model predictions in mice (healthy and infected) and rats were in reasonable agreement with experimental data, and all predicted AUCs in plasma and tissues met the two-fold acceptance criteria relative to observations. To further explore the distribution of spectinamide 1599 within granuloma substructures as encountered in tuberculosis, we utilized the Simcyp granuloma model combined with model predictions in our PBPK model. Simulation results suggest substantial exposure in all lesion substructures, with particularly high exposure in the rim area and macrophages. The developed model may be leveraged as an effective tool in identifying optimal dose levels and dosing regimens of spectinamides for further preclinical and clinical development.

Binding of dicoumarol analog with DNA and its antioxidant studies: A biophysical insight by in-vitro and in-silico approaches

DNA is the major target for a number of pharmaceutical drugs. The interaction of drug molecules with DNA plays a major role in pharmacokinetics and pharmacodynamics. Bis-coumarin derivatives have diverse biological properties. Here, we have explored the antioxidant activity of 3,3'-Carbonylbis (7-diethylamino coumarin) (CDC) using DPPH, H₂O_2, and superoxide scavenging studies followed by its binding mode in calf thymus-DNA (CT-DNA) using several biophysical methods including molecular docking. CDC exhibited comparable antioxidant activity to standard ascorbic acid. The UV-Visible and fluorescence spectral variations indicate the CDC-DNA complex formation. The binding constant in the range of 10⁴ M^-1 was obtained from spectroscopic studies at room temperature. The fluorescence quenching of CDC by CT-DNA suggested a quenching constant (K_SV) of 10³ to 10⁴ M^-1 order. Thermodynamic studies at 303, 308, and 318 K revealed the observed quenching as a dynamic process besides the spontaneity of the interaction with negative free energy change. Competitive binding studies with site markers like ethidium bromide, methylene blue, and Hoechst 33258 reflect CDC's groove mode of interaction. The result was complemented by DNA melting study, viscosity measurement, and KI quenching studies. The ionic strength effect was studied to interpret the electrostatic interaction and found its insignificant role in the binding. Molecular docking studies suggested the binding location of CDC within the minor groove of CT-DNA, complementing the experimental result.