Good Practices in Model-Informed Drug Discovery and Development: Practice, Application, and Documentation

Bioequivalence risk assessment of oral formulations containing racemic ibuprofen through a chiral physiologically based pharmacokinetic model of ibuprofen enantiomers

The characterization of the time course of ibuprofen enantiomers can be useful in the selection of the most sensitive analyte in bioequivalence studies. Physiologically based pharmacokinetic (PBPK) modelling and simulation represents the most efficient methodology to virtually assess bioequivalence outcomes. In this work, we aim to develop and verify a PBPK model for ibuprofen enantiomers administered as a racemic mixture with different immediate release dosage forms to anticipate bioequivalence outcomes based on different particle size distributions. A PBPK model incorporating stereoselectivity and non-linearity in plasma protein binding and metabolism as well as R-to-S unidirectional inversion has been developed in Simcyp®. A dataset composed of 11 Phase I clinical trials with 54 scenarios (27 per enantiomer) and 14,452 observations (7129 for R-ibuprofen and 7323 for S-ibuprofen) was used. Prediction errors for AUC0-t and Cmax for both enantiomers fell within the 0.8-1.25 range in 50/54 (93 %) and 42/54 (78 %) of scenarios, respectively. Outstanding model performance, with 10/10 (100 %) of Cmax and 9/10 (90 %) of AUC0-t within the 0.9-1.1 range, was demonstrated for oral suspensions, which strongly supported its use for bioequivalence risk assessment. The deterministic bioequivalence risk assessment has revealed R-ibuprofen as the most sensitive analyte to detect differences in particle size distribution for oral suspensions containing 400 mg of racemic ibuprofen, suggesting that achiral bioanalytical methods would increase type II error and declare non-bioequivalence for formulations that are bioequivalent for the eutomer.

Insights into drug development with quantitative systems pharmacology: A prospective case study of uncovering hyperkalemia risk in diabetic nephropathy with virtual clinical trials

The quantitative systems pharmacology (QSP) approach is widely applied to address various essential questions in drug discovery and development, such as identification of the mechanism of action of a therapeutic agent, patient stratification, and the mechanistic understanding of the progression of disease. In this review article, we show the current landscape of the application of QSP modeling using a survey of QSP publications over 10 years from 2013 to 2022. We also present a use case for the risk assessment of hyperkalemia in patients with diabetic nephropathy treated with mineralocorticoid receptor antagonists (MRAs, renin-angiotensin-aldosterone system inhibitors), as a prospective simulation of late clinical development. A QSP model for generating virtual patients with diabetic nephropathy was used to quantitatively assess that the nonsteroidal MRAs, finerenone and apararenone, have a lower risk of hyperkalemia than the steroidal MRA, eplerenone. Prospective simulation studies using a QSP model are useful to prioritize pharmaceutical candidates in clinical development and validate mechanism-based pharmacological concepts related to the risk-benefit, before conducting large-scale clinical trials.

Artificial Intelligence for Quantitative Modeling in Drug Discovery and Development: An Innovation and Quality Consortium Perspective on Use Cases and Best Practices

Recent breakthroughs in artificial intelligence (AI) and machine learning (ML) have ushered in a new era of possibilities across various scientific domains. One area where these advancements hold significant promise is model-informed drug discovery and development (MID3). To foster a wider adoption and acceptance of these advanced algorithms, the Innovation and Quality (IQ) Consortium initiated the AI/ML working group in 2021 with the aim of promoting their acceptance among the broader scientific community as well as by regulatory agencies. By drawing insights from workshops organized by the working group and attended by key stakeholders across the biopharma industry, academia, and regulatory agencies, this white paper provides a perspective from the IQ Consortium. The range of applications covered in this white paper encompass the following thematic topics: (i) AI/ML-enabled Analytics for Pharmacometrics and Quantitative Systems Pharmacology (QSP) Workflows; (ii) Explainable Artificial Intelligence and its Applications in Disease Progression Modeling; (iii) Natural Language Processing (NLP) in Quantitative Pharmacology Modeling; and (iv) AI/ML Utilization in Drug Discovery. Additionally, the paper offers a set of best practices to ensure an effective and responsible use of AI, including considering the context of use, explainability and generalizability of models, and having human-in-the-loop. We believe that embracing the transformative power of AI in quantitative modeling while adopting a set of good practices can unlock new opportunities for innovation, increase efficiency, and ultimately bring benefits to patients.

Innovative research methodologies in the EU regulatory framework: an analysis of EMA qualification procedures from a pediatric perspective

Introduction

The European Medicines Agency (EMA) offers scientific advice to support the qualification procedure of novel methodologies, such as preclinical and in vitro models, biomarkers, and pharmacometric methods, thereby endorsing their acceptability in medicine research and development (R&D). This aspect is particularly relevant to overcome the scarcity of data and the lack of validated endpoints and biomarkers in research fields characterized by small samples, such as pediatrics.

Aim

This study aimed to analyze the potential pediatric interest in methodologies qualified as "novel methodologies for medicine development" by the EMA.

Methods

The positive qualification opinions of novel methodologies for medicine development published on the EMA website between 2008 and 2023 were identified. Multi-level analyses were conducted to investigate data with a hierarchical structure and the effects of cluster-level variables and cluster-level variances and to evaluate their potential pediatric interest, defined as the possibility of using the novel methodology in pediatric R&D and the availability of pediatric data. The duration of the procedure, the type of methodology, the specific disease or disease area addressed, the type of applicant, and the availability of pediatric data at the time of the opinion release were also investigated.

Results

Most of the 27 qualifications for novel methodologies issued by the EMA (70%) were potentially of interest to pediatric patients, but only six of them reported pediatric data. The overall duration of qualification procedures with pediatric interest was longer than that of procedures without any pediatric interest (median time: 7 months vs. 3.5 months, respectively; p = 0.082). In parallel, qualification procedures that included pediatric data lasted for a longer period (median time: 8 months vs. 6 months, respectively; p = 0.150). Nephrology and neurology represented the main disease areas (21% and 16%, respectively), while endpoints, biomarkers, and registries represented the main types of innovative methodologies (32%, 26%, and 16%, respectively).

Discussion

Our results underscore the importance of implementing innovative methodologies in regulatory-compliant pediatric research activities. Pediatric-dedicated research infrastructures providing regulatory support and strategic advice during research activities could be crucial to the design of ad hoc pediatric methodologies or to extend and validate them for pediatrics.

Gut Microbiome Integration in Drug Discovery and Development of Small Molecules

Human microbiomes, particularly in the gut, could have a major impact on the efficacy and toxicity of drugs. However, gut microbial metabolism is often neglected in the drug discovery and development process. Medicen, a Paris-based human health innovation cluster, has gathered more than 30 international leading experts from pharma, academia, biotech, clinical research organizations, and regulatory science to develop proposals to facilitate the integration of microbiome science into drug discovery and development. Seven subteams were formed to cover the complementary expertise areas of 1) pharma experience and case studies, 2) in silico microbiome-drug interaction, 3) in vitro microbial stability screening, 4) gut fermentation models, 5) animal models, 6) microbiome integration in clinical and regulatory aspects, and 7) microbiome ecosystems and models. Each expert team produced a state-of-the-art report of their respective field highlighting existing microbiome-related tools at every stage of drug discovery and development. The most critical limitations are the growing, but still limited, drug-microbiome interaction data to produce predictive models and the lack of agreed-upon standards despite recent progress. In this paper we will report on and share proposals covering 1) how microbiome tools can support moving a compound from drug discovery to clinical proof-of-concept studies and alert early on potential undesired properties stemming from microbiome-induced drug metabolism and 2) how microbiome data can be generated and integrated in pharmacokinetic models that are predictive of the human situation. Examples of drugs metabolized by the microbiome will be discussed in detail to support recommendations from the working group. SIGNIFICANCE STATEMENT: Gut microbial metabolism is often neglected in the drug discovery and development process despite growing evidence of drugs' efficacy and safety impacted by their interaction with the microbiome. This paper will detail existing microbiome-related tools covering every stage of drug discovery and development, current progress, and limitations, as well as recommendations to integrate them into the drug discovery and development process.

Bioavailability of three novel oral, sustained-release pellets, relative to an immediate-release tablet containing 500 mg flucytosine: A randomized, open-label, crossover study in healthy volunteers

The opportunistic fungal infection cryptococcal meningoencephalitis is a major cause of death among people living with HIV in sub-Saharan Africa. We report pharmacokinetic (PK) and safety data from a randomized, four-period crossover phase I trial of three sustained-release (SR) oral pellet formulations of 5-flucytosine conducted in South Africa. These formulations were developed to require less frequent administration, to provide a convenient alternative to the current immediate release (IR) formulation, A. Formulations B, C, and D were designed to release 5-flucytosine as a percentage of the nominal dose in vitro. We assessed their safety and PK profiles in a single dose (1 × 3000 mg at 0 h), relative to commercial IR tablets (Ancotil 500 mg tablets; 3 × 500 mg at 0 h and 3 × 500 mg at 6 h) in healthy, fasted participants. Forty-two healthy participants were included. All treatments were well-tolerated. The primary PK parameters, maximum observed plasma concentration (Cmax ) and area under the concentration-time profiles, were significantly lower for the SR formulations than for the IR tablets, and the geometric mean ratios fell outside the conventional bioequivalence limits. The median maximum time to Cmax was delayed for the SR pellets. Physiologically-based PK modeling indicated a twice-daily 6400 mg dose of SR formulation D in fasted condition would be optimal for further clinical development. This regimen is predicted to result in a rapid steady-state plasma exposure with effective and safe trough plasma concentration and Cmax values, within the therapeutic boundaries relative to plasma exposure after four times per day administration of IR tablets (PACTR202201760181404).

Pharmacokinetics and pharmacodynamics of finerenone in patients with chronic kidney disease and type 2 diabetes: Insights based on FIGARO-DKD and FIDELIO-DKD

AIMS

To perform dose-exposure-response analyses to determine the effects of finerenone doses.

MATERIALS AND METHODS

Two randomized, double-blind, placebo-controlled phase 3 trials enrolling 13 026 randomized participants with type 2 diabetes (T2D) from global sites, each with an estimated glomerular filtration rate (eGFR) of 25 to 90 mL/min/1.73 m2 , a urine albumin-creatinine ratio (UACR) of 30 to 5000 mg/g, and serum potassium ≤ 4.8 mmol/L were included. Interventions were titrated doses of finerenone 10 or 20 mg versus placebo on top of standard of care. The outcomes were trajectories of plasma finerenone and serum potassium concentrations, UACR, eGFR and kidney composite outcomes, assessed using nonlinear mixed-effects population pharmacokinetic (PK)/pharmacodynamic (PD) and parametric time-to-event models.

RESULTS

For potassium, lower serum levels and lower rates of hyperkalaemia were associated with higher doses of finerenone 20 mg compared to 10 mg (p < 0.001). The PK/PD model analysis linked this observed inverse association to potassium-guided dose titration. Simulations of a hypothetical trial with constant finerenone doses revealed a shallow but increasing exposure-potassium response relationship. Similarly, increasing finerenone exposures led to less than dose-proportional increasing reductions in modelled UACR. Modelled UACR explained 95% of finerenone's treatment effect in slowing chronic eGFR decline. No UACR-independent finerenone effects were identified. Neither sodium-glucose cotransporter-2 (SGLT2) inhibitor nor glucagon-like peptide-1 receptor agonist (GLP-1RA) treatment significantly modified the effects of finerenone in reducing UACR and eGFR decline. Modelled eGFR explained 87% of finerenone's treatment effect on kidney outcomes. No eGFR-independent effects were identified.

CONCLUSIONS

The analyses provide strong evidence for the effectiveness of finerenone dose titration in controlling serum potassium elevations. UACR and eGFR are predictive of kidney outcomes during finerenone treatment. Finerenone's kidney efficacy is independent of concomitant use of SGLT2 inhibitors and GLP-1RAs.

In silico trial for the assessment of givinostat dose adjustment rules based on the management of key hematological parameters in polycythemia vera patients

Polycythemia vera (PV) is a chronic myeloproliferative neoplasm characterized by excessive levels of platelets (PLT), white blood cells (WBC), and hematocrit (HCT). Givinostat (ITF2357) is a potent histone-deacetylase inhibitor that showed a good safety/efficacy profile in PV patients during phase I/II studies. A phase III clinical trial had been planned and an adaptive dosing protocol had been proposed where givinostat dose is iteratively adjusted every 28 days (one cycle) based on PLT, WBC, and HCT. As support, a simulation platform to evaluate and refine the proposed givinostat dose adjustment rules was developed. A population pharmacokinetic/pharmacodynamic model predicting the givinostat effects on PLT, WBC, and HCT in PV patients was developed and integrated with a control algorithm implementing the adaptive dosing protocol. Ten in silico trials in ten virtual PV patient populations were simulated 500 times. Considering an eight-treatment cycle horizon, reducing/increasing the givinostat daily dose by 25 mg/day step resulted in a higher percentage of patients with a complete hematological response (CHR), that is, PLT ≤400 × 109 /L, WBC ≤10 × 109 /L, and HCT < 45% without phlebotomies in the last three cycles, and a lower percentage of patients with grade II toxicity events compared with 50 mg/day adjustment steps. After the eighth cycle, 85% of patients were predicted to receive a dose ≥100 mg/day and 40.90% (95% prediction interval = [34, 48.05]) to show a CHR. These results were confirmed at the end of 12th, 18th, and 24th cycles, showing a stability of the response between the eighth and 24th cycles.

An integrative mechanistic model of thymocyte dynamics

Background

The thymus plays a central role in shaping human immune function. A mechanistic, quantitative description of immune cell dynamics and thymic output under homeostatic conditions and various patho-physiological scenarios are of particular interest in drug development applications, e.g., in the identification of potential therapeutic targets and selection of lead drug candidates against infectious diseases.

Methods

We here developed an integrative mathematical model of thymocyte dynamics in human. It incorporates mechanistic features of thymocyte homeostasis as well as spatial constraints of the thymus and considerations of age-dependent involution. All model parameter estimates were obtained based on published physiological data of thymocyte dynamics and thymus properties in mouse and human. We performed model sensitivity analyses to reveal potential therapeutic targets through an identification of processes critically affecting thymic function; we further explored differences in thymic function across healthy subjects, multiple sclerosis patients, and patients on fingolimod treatment.

Results

We found thymic function to be most impacted by the egress, proliferation, differentiation and death rates of those thymocytes which are most differentiated. Model predictions also showed that the clinically observed decrease in relapse risk with age, in multiple sclerosis patients who would have discontinued fingolimod therapy, can be explained mechanistically by decreased thymic output with age. Moreover, we quantified the effects of fingolimod treatment duration on thymic output.

Conclusions

In summary, the proposed model accurately describes, in mechanistic terms, thymic output as a function of age. It may be further used to perform predictive simulations of clinically relevant scenarios which combine specific patho-physiological conditions and pharmacological interventions of interest.

Pediatric oncology drug development and dosage optimization

Oncology drug discovery and development has always been an area facing many challenges. Phase 1 oncology studies are typically small, open-label, sequential studies enrolling a small sample of adult patients (i.e., 3-6 patients/cohort) in dose escalation. Pediatric evaluations typically lag behind the adult development program. The pediatric starting dose is traditionally referenced on the recommended phase 2 dose in adults with the incorporation of body size scaling. The size of the study is also small and dependent upon the prevalence of the disease in the pediatric population. Similar to adult development, the dose is escalated or de-escalated until reaching the maximum tolerated dose (MTD) that also provides desired biological activities or efficacy. The escalation steps and identification of MTD are often rule-based and do not incorporate all the available information, such as pharmacokinetic (PK), pharmacodynamic (PD), tolerability and efficacy data. Therefore, it is doubtful if the MTD approach is optimal to determine the dosage. Hence, it is important to evaluate whether there is an optimal dosage below the MTD, especially considering the emerging complexity of combination therapies and the long-term tolerability and safety of the treatments. Identification of an optimal dosage is also vital not only for adult patients but for pediatric populations as well. Dosage-finding is much more challenging for pediatric populations due to the limited patient population and differences among the pediatric age range in terms of maturation and ontogeny that could impact PK. Many sponsors defer the pediatric strategy as they are often perplexed by the challenges presented by pediatric oncology drug development (model of action relevancy to pediatric population, budget, timeline and regulatory requirements). This leads to a limited number of approved drugs for pediatric oncology patients. This review article provides the current regulatory landscape, incentives and how they impact pediatric drug discovery and development. We also consider different pediatric cancers and potential clinical trial challenges/opportunities when designing pediatric clinical trials. An outline of how quantitative methods such as pharmacometrics/modelling & simulation can support the dosage-finding and justification is also included. Finally, we provide some reflections that we consider helpful to accelerate pediatric drug discovery and development.

Experience Learned and Perspectives on Using Model-Integrated Evidence in the Regulatory Context for Generic Drug Products-a Meeting Report

This report summarizes relevant insights and discussions from a 2022 FDA public workshop titled Best Practices for Utilizing Modeling Approaches to Support Generic Product Development which illustrated how model-integrated evidence has been used and can be leveraged further to inform generic drug product development and regulatory decisions during the assessment of generic drug applications submitted to the FDA. The workshop attendees discussed that model-integrated evidence (MIE) approaches for generics are being applied in the space of long-acting injectable (LAI) products to develop shorter and more cost-effective alternative study designs for LAI products. Modeling and simulation approaches are utilized to support virtual BE assessments at the site of action for locally acting drug products and to assess the impact of food on BE assessments for oral dosage forms. The factors contributing to the success of the model-informed drug development program under PDUFA VI were discussed. The generic drug industry shared that decisions on formulation candidate/formulation variant selection, on pilot in vivo bioavailability studies, and on alternative study designs for BE assessment are informed by modeling and simulation approaches. There was agreement that interactions between the regulatory agencies and the industry are desirable because they improve the industry's understanding of scientific and other regulatory considerations on implementing modeling and simulation approaches in drug development and regulatory submissions.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

In Silico Clinical Trials: Is It Possible?

Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.

Tutorial on model selection and validation of model input into precision dosing software for model-informed precision dosing

There has been rising interest in using model-informed precision dosing to provide personalized medicine to patients at the bedside. This methodology utilizes population pharmacokinetic models, measured drug concentrations from individual patients, pharmacodynamic biomarkers, and Bayesian estimation to estimate pharmacokinetic parameters and predict concentration-time profiles in individual patients. Using these individualized parameter estimates and simulated drug exposure, dosing recommendations can be generated to maximize target attainment to improve beneficial effect and minimize toxicity. However, the accuracy of the output from this evaluation is highly dependent on the population pharmacokinetic model selected. This tutorial provides a comprehensive approach to evaluating, selecting, and validating a model for input and implementation into a model-informed precision dosing program. A step-by-step outline to validate successful implementation into a precision dosing tool is described using the clinical software platforms Edsim++ and MwPharm++ as examples.

PK/PD model-informed dose selection for oncology phase I expansion: Case study based on PF-06939999, a PRMT5 inhibitor

The optimal dose for targeted oncology therapeutics is often not the maximum tolerated dose. Pharmacokinetic/pharmacodynamic (PK/PD) modeling can be an effective tool to integrate clinical data to help identify the optimal dose. This case study shows the utility of population PK/PD modeling in selecting the recommended dose for expansion (RDE) for the first-in-patient (FIP) study of PF-06939999, a small-molecule inhibitor of protein arginine methyltransferase 5. In the dose escalation part of the FIP trial (NCT03854227), 28 patients with solid tumors were administered PF-06939999 at 0.5 mg, 4 mg, 6 mg, or 8 mg once daily (q.d.) or 0.5 mg, 1 mg, 2 mg, 4 mg, or 6 mg twice daily (b.i.d.). Tolerability, safety, PK, PD biomarkers (plasma symmetrical dimethyl-arginine [SDMA]), and antitumor response were assessed. Semimechanistic population PK/PD modeling analyses were performed to characterize the time-courses of plasma PF-06939999 concentrations, plasma SDMA, and platelet counts collected from 28 patients. Platelet counts were evaluated because thrombocytopenia was the treatment-related adverse event with clinical safety concern. The models adequately described the PK, SDMA, and platelet count profiles both at individual and population levels. Simulations suggested that among a range of dose levels, 6 mg q.d. would yield the optimal balance between achieving the PD target (i.e., 78% reduction in plasma SDMA) and staying below an acceptable probability of developing grade ≥3 thrombocytopenia. As a result, 6 mg q.d. was selected as the RDE. The model-informed drug development approach informed the rational dose selection for the early clinical development of PF-06939999.

Skipping a pillar does not make for strong foundations: Pharmacokinetic-pharmacodynamic reasoning behind the shape of dose-response relationships in oncology

Dose-response analysis is often applied to the quantification of drug-effect especially for slowly responding disease end points where a comparison is made across dose levels after a particular period of treatment. It has long been recognized that exposure - response is more appropriate than dose-response. However, trials necessarily are designed as dose-response experiments. Second, a wide range of functional forms are used to express relationships between dose and response. These considerations are also important for clinical development because pharmacokinetic (PK; and variability) plus pharmacokinetic-pharmacodynamic modeling may allow one to anticipate the shape of the dose-response curve and so the trial design. Here, we describe how the location and steepness of the dose response is determined by the PKs of the compound being tested and its exposure-response relationship in terms of potency (location), efficacy (maximum effect) and Hill coefficient (steepness). Thus, the location (50% effective dose [ED50 ]) is dependent not only on the potency (half-maximal effective concentration) but also the compound's PKs. Similarly, the steepness of the dose response is shown to be a function of the half-life of the drug. It is also shown that the shape of relationship varies dependent on the assumed time course of the disease. This is important in the context of drug-discovery where the in vivo potencies of compounds are compared as well as when considering an analysis of summary data (for example, model-based meta-analysis) for clinical decision making.

Improving pharmacometrics analysis efficiency using DataCheQC: An interactive, Shiny-based app for quality control of pharmacometrics datasets

DataCheQC is an interactive application based on the R Shiny framework developed for the purposes of performing quality control (QC) checks on pharmacometric datasets, and thereby supporting the implementation of model-informed drug development. Features include visual inspection of variables and data entries for errors and/or anomalies, and ensuring structural integrity through comparison with a dataset specification file. The app, which requires no programming knowledge to operate, allows the user to collect all findings into a summary report downloadable directly from the app itself. The source code for the app is freely available on GitHub under an open-source license (https://github.com/DotanOr/DataCheQC) and can also be accessed online (https://dotanor.shinyapps.io/DataCheQC/).

Generation and application of avatars in pharmacometric modelling

Simulations from population models have critical applications in drug discovery and development. Avatars or digital twins, defined as individual simulations matching clinical criteria of interest compared to observations from a real subject within a predefined margin of accuracy, may be a better option for simulations performed to inform future drug development stages in cases where an adequate model is not achievable. The aim of this work was to (1) investigate methods for generating avatars with pharmacometric models, and (2) explore the properties of the generated avatars to assess the impact of the different selection settings on the number of avatars per subject, their closeness to the individual observations, and the properties of the selected samples subset from the theoretical model parameters probability density function. Avatars were generated using different combinations of nature and number of clinical criteria, accuracy of agreement, and/or number of simulations for two examples models previously published (hemato-toxicity and integrated glucose-insulin model). The avatar distribution could be used to assess the appropriateness of the models assumed parameter distribution. Similarly it could be used to assess the models ability to properly describe the trajectories of the observations. Avatars can give nuanced information regarding the ability of a model to simulate data similar to the observations both at the population and at the individual level. Further potential applications for avatars may be as a diagnostic tool, an alternative to simulations with insurance to replicate key clinical features, and as an individual measure of model fit.

A two-stages global sensitivity analysis by using the δ sensitivity index in presence of correlated inputs: application on a tumor growth inhibition model based on the dynamic energy budget theory

Global sensitivity analysis (GSA) evaluates the impact of variability and/or uncertainty of the model parameters on given model outputs. GSA is useful for assessing the quality of Pharmacometric model inference. Indeed, model parameters can be affected by high (estimation) uncertainty due to the sparsity of data. Independence between model parameters is a common assumption of GSA methods. However, ignoring (known) correlations between parameters may alter model predictions and, then, GSA results. To address this issue, a novel two-stages GSA technique based on the δ index, which is well-defined also in presence of correlated parameters, is here proposed. In the first step, statistical dependencies are neglected to identify parameters exerting causal effects. Correlations are introduced in the second step to consider the real distribution of the model output and investigate also the 'indirect' effects due to the correlation structure. The proposed two-stages GSA strategy was applied, as case study, to a preclinical tumor-in-host-growth inhibition model based on the Dynamic Energy Budget theory. The aim is to evaluate the impact of the model parameter estimate uncertainty (including correlations) on key model-derived metrics: the drug threshold concentration for tumor eradication, the tumor volume doubling time and a new index evaluating the drug efficacy-toxicity trade-off. This approach allowed to rank parameters according to their impact on the output, discerning whether a parameter mainly exerts a causal or 'indirect' effect. Thus, it was possible to identify uncertainties that should be necessarily reduced to obtain robust predictions for the outputs of interest.

An AI Approach to Generating MIDD Assets Across the Drug Development Continuum

Model-informed drug development involves developing and applying exposure-based, biological, and statistical models derived from preclinical and clinical data sources to inform drug development and decision-making. Discrete models are generated from individual experiments resulting in a single model expression that is utilized to inform a single stage-gate decision. Other model types provide a more holistic view of disease biology and potentially disease progression depending on the appropriateness of the underlying data sources for that purpose. Despite this awareness, most data integration and model development approaches are still reliant on internal (within company) data stores and traditional structural model types. An AI/ML-based MIDD approach relies on more diverse data and is informed by past successes and failures including data outside a host company (external data sources) that may enhance predictive value and enhance data generated by the sponsor to reflect more informed and timely experimentation. The AI/ML methodology also provides a complementary approach to more traditional modeling efforts that support MIDD and thus yields greater fidelity in decision-making. Early pilot studies support this assessment but will require broader adoption and regulatory support for more evidence and refinement of this paradigm. An AI/ML-based approach to MIDD has the potential to transform regulatory science and the current drug development paradigm, optimize information value, and increase candidate and eventually product confidence with respect to safety and efficacy. We highlight early experiences with this approach using the AI compute platforms as representative examples of how MIDD can be facilitated with an AI/ML approach.

Clozapine-loaded nanocapsules improve antipsychotic activity in rats: building a sequential PopPK/PD model to discriminate nanocarriers in the preformulation step

PURPOSE

We investigated the impact of nanoformulations on the dose-exposure-response relationship of clozapine (CZP), a low-solubility antipsychotic with serious adverse effects, using a popPK/PD approach.

METHODS

We evaluated the pharmacokinetics and PK/PD profiles of three coated polymeric CZP-loaded nanocapsules functionalized with polysorbate 80 (NCP80), polyethylene glycol (NCPEG), and chitosan (NCCS). Data on in vitro CZP release by dialysis bag, plasma pharmacokinetic profiles in male Wistar rats (n = 7/group, 5 mg kg-1, i.v.), and percentage of head movements in a stereotyped model (n = 7/group, 5 mg kg-1, i.p.) were integrated using a sequential model building approach (MonolixSuiteTM-2020R1-Simulation Plus).

RESULTS

A base popPK model developed with CZP solution data collected after the i.v. administration of CZP was expanded to describe the changes in drug distribution caused by nanoencapsulation. Two additional compartments were inserted into the NCP80 and NCPEG models, and a third compartment was included in the NCCS model. The nanoencapsulation showed a decrease in the central volume of distribution for NCCS (V1NCpop = 0.21 mL), while for FCZP, NCP80, and NCPEG, it was ~1 mL. The peripheral distribution volume was higher for the nanoencapsulated groups (19.1 and 129.45 mL for NCCS and NCP80, respectively) than for FCZP. The popPK/PD model showed a formulation-dependent plasma IC50, with 20-, 50-, and 80-fold reductions compared to the CZP solution (NCP80, NCPEG, and NCCS, respectively).

CONCLUSION

Our model discriminates the coatings and describes the peculiar PK and PD behavior of nanoencapsulated CZP, especially NCCS, making it an exciting tool for evaluating the preclinical performance of nanoparticles.

Disease progression joint model predicts time to type 1 diabetes onset: Optimizing future type 1 diabetes prevention studies

Clinical trials seeking type 1 diabetes prevention are challenging in terms of identifying patient populations likely to progress to type 1 diabetes within limited (i.e., short-term) trial durations. Hence, we sought to improve such efforts by developing a quantitative disease progression model for type 1 diabetes. Individual-level data obtained from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies were used to develop a joint model that links the longitudinal glycemic measure to the timing of type 1 diabetes diagnosis. Baseline covariates were assessed using a stepwise covariate modeling approach. Our study focused on individuals at risk of developing type 1 diabetes with the presence of two or more diabetes-related autoantibodies (AAbs). The developed model successfully quantified how patient features measured at baseline, including HbA1c and the presence of different AAbs, alter the timing of type 1 diabetes diagnosis with reasonable accuracy and precision (<30% RSE). In addition, selected covariates were statistically significant (p < 0.0001 Wald test). The Weibull model best captured the timing to type 1 diabetes diagnosis. The 2-h oral glucose tolerance values assessed at each visit were included as a time-varying biomarker, which was best quantified using the sigmoid maximum effect function. This model provides a framework to quantitatively predict and simulate the time to type 1 diabetes diagnosis in individuals at risk of developing the disease and thus, aligns with the needs of pharmaceutical companies and scientists seeking to advance therapies aimed at interdicting the disease process.

Safety and pharmacokinetics of IBI112, an IL-23 monoclonal antibody, in Chinese healthy volunteers: a first-in-human phase 1 study

BACKGROUND

Interleukin (IL) 23p19 monoclonal antibodies were efficacious and safe in the treatment of psoriasis. A first-in-human (FIH) study was conducted to evaluate the safety, tolerability, pharmacokinetics (PK) and immunogenicity of IBI112, a novel IL-23p19 monoclonal antibody.

METHODS

In this FIH, randomized, double-blind, placebo-controlled, single-ascending-dose study, a subcutaneous (SC, 5-600 mg) or intravenous (IV, 100 and 600 mg) or placebo was administered to eligible healthy subjects. Safety was assessed by physical examinations, vital signs, laboratory tests, and electrocardiograms. Furthermore, non-compartment analysis and population PK modeling were conducted to characterize PK, and model-based simulation was applied to justify dose selection for psoriasis patients.

RESULTS

A total of 46 subjects were enrolled, with 35 receiving IBI112 and 11 receiving placebo. No serious adverse events (SAEs) and no clinically significant adverse events were identified. After a single SC of IBI112, the median Tmax was 4-10.5 days, and the half-life (t1/2) ranged from 21.8 to 35.8 days. IBI112 exposures (Cmax and AUCinf) approached dose proportionality across 5-300 mg range.

CONCLUSION

IBI112 was well tolerated and safe at SC or IV dose up to 600 mg and showed a linear PK characteristics at SC dose from 5 to 300 mg.

CLINICAL TRIAL REGISTRATION

ClinicalTrial.gov NCT04511624.

Virtual patients, digital twins and causal disease models: paving the ground for in silico clinical trials

Computational models are being explored to simulate in silico the efficacy and safety of drug candidates and medical devices. Disease models that are based on patients' profiling data are being produced to represent interactomes of genes or proteins and to infer causality in the pathophysiology {AuQ: Edit OK?}, which makes it possible to mimic the impact of drugs on relevant targets. Virtual patients designed from medical records as well as digital twins were generated to simulate specific organs and to predict treatment efficacy at the individual patient level {AuQ: Edit OK?}. As the acceptance of digital evidence by regulators grows, predictive artificial intelligence (AI)-based models will support the design of confirmatory trials in humans and will accelerate the development of efficient drugs and medical devices.

Deep Learning Methods Applied to Drug Concentration Prediction of Olanzapine

Pharmacometrics and the utilization of population pharmacokinetics play an integral role in model-informed drug discovery and development (MIDD). Recently, there has been a growth in the application of deep learning approaches to aid in areas within MIDD. In this study, a deep learning model, LSTM-ANN, was developed to predict olanzapine drug concentrations from the CATIE study. A total of 1527 olanzapine drug concentrations from 523 individuals along with 11 patient-specific covariates were used in model development. The hyperparameters of the LSTM-ANN model were optimized through a Bayesian optimization algorithm. A population pharmacokinetic model using the NONMEM model was constructed as a reference to compare to the performance of the LSTM-ANN model. The RMSE of the LSTM-ANN model was 29.566 in the validation set, while the RMSE of the NONMEM model was 31.129. Permutation importance revealed that age, sex, and smoking were highly influential covariates in the LSTM-ANN model. The LSTM-ANN model showed potential in the application of drug concentration predictions as it was able to capture the relationships within a sparsely sampled pharmacokinetic dataset and perform comparably to the NONMEM model.

Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment

In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

In Silico Pharmacology for Evidence-Based and Precision Medicine

Personalized/precision medicine (PM) originates from the application of molecular pharmacology in clinical practice, representing a new era in healthcare that aims to identify and predict optimum treatment outcomes for a patient or a cohort with similar genotype/phenotype characteristics [...].

Application of Population Balance Model to Simulate Precipitation of Weak Base and Zwitterionic Drugs in Gastrointestinal pH Environment

The purpose of the present study was to evaluate whether the population balance model (PBM) could be a suitable model for the precipitation of weak base and zwitterionic drugs in the gastrointestinal pH environment. Five poorly soluble drugs were used as model drugs (dipyridamole, haloperidol, papaverine, phenazopyridine, and tosufloxacin). PBM consists of the equations for primary nucleation, secondary nucleation, and particle growth. Each equation has two empirical parameters. The pH shift (pH-dumping) precipitation test (pH 3.0 to 6.5) was used to determine the model parameters for each drug. It was difficult to determine all six parameters by simultaneously fitting them to the precipitation profiles. Therefore, the number of model parameters was reduced from six to three by neglecting the secondary nucleation process and applying a common exponent number for the particle growth equation. Despite reducing the parameter number, PBM appropriately described the precipitation profiles in the pH shift tests. The constructed PBM model was then used to predict the precipitation profiles in an artificial stomach-intestine transfer (ASIT) test. PBM appropriately predicted the precipitation profiles in the ASIT test. These results suggested that PBM can be a suitable model to represent the precipitation of weak base and zwitterionic drugs in the gastrointestinal pH environment for biopharmaceutics modeling and simulation.

Using Machine Learning To Predict Partition Coefficient (Log P) and Distribution Coefficient (Log D) with Molecular Descriptors and Liquid Chromatography Retention Time

During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log P, and distribution coefficient, Log D, which are useful in estimating the distribution of drugs within the body. Log P and Log D are traditionally measured using the shake-flask method and high-performance liquid chromatography. However, it is challenging to measure these properties for species that are very hydrophobic (or hydrophilic) owing to the very low equilibrium concentrations partitioned into octanol (or aqueous) phases. Moreover, the shake-flask method is relatively time-consuming and can require multistep dilutions as the range of analyte concentrations can differ by several orders of magnitude. Here, we circumvent these limitations by using machine learning (ML) to correlate Log P and Log D with liquid chromatography (LC) retention time (RT). Predictive models based on four ML algorithms, which used molecular descriptors and LC RTs as features, were extensively tested and compared. The inclusion of RT as an additional descriptor improves model performance (MAE = 0.366 and R² = 0.89), and Shapley additive explanations analysis indicates that RT has the highest impact on model accuracy.

A "middle-out approach" for the prediction of human drug disposition from preclinical data using simplified physiologically based pharmacokinetic (PBPK) models

Simplified physiologically based pharmacokinetic (PBPK) models using estimated tissue-to-unbound plasma partition coefficients (Kpus) were previously investigated by fitting them to in vivo pharmacokinetic (PK) data. After optimization with preclinical data, the performance of these models for extrapolation of distribution kinetics to human were evaluated to determine the best approach for the prediction of human drug disposition and volume of distribution (Vss) using PBPK modeling. Three lipophilic bases were tested (diazepam, midazolam, and basmisanil) for which intravenous PK data were available in rat, monkey, and human. The models with Kpu scalars using k-means clustering were generally the best for fitting data in the preclinical species and gave plausible Kpu values. Extrapolations of plasma concentrations for diazepam and midazolam using these models and parameters obtained were consistent with the observed clinical data. For diazepam and midazolam, the human predictions of Vss after optimization in rats and monkeys were better compared with the Vss estimated from the traditional PBPK modeling approach (varying from 1.1 to 3.1 vs. 3.7-fold error). For basmisanil, the sparse preclinical data available could have affected the model performance for fitting and the subsequent extrapolation to human. Overall, this work provides a rational strategy to predict human drug distribution using preclinical PK data within the PBPK modeling strategy.

A Model-Informed Approach to Accelerate the Clinical Development of Janagliflozin, an Innovative SGLT2 Inhibitor

AIM

To apply model-informed drug development (MIDD) approach to support the decision making in drug development and accelerate the clinical development of janagliflozin, an orally selective SGLT2 inhibitor.

METHOD

We previously developed a mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model of janagliflozin based on preclinical data to optimize dose design in the first-in-human (FIH) study. In the current study, we used clinical PK/PD data of the FIH study to validate the model and then simulate the PK/PD profiles of multiple ascending dosing (MAD) study in healthy subjects. Besides, we developed a population PK/PD model of janagliflozin to predict steady-state urinary glucose excretion (UGE [UGE,ss]) in healthy subjects in the Phase 1 stage. This model was subsequently used to simulate the UGE, ss in patients with type 2 diabetes mellitus (T2DM) based on a unified PD target (ΔUGEc) across healthy subjects and patients with T2DM. This unified PD target was estimated from our previous work of model-based meta-analysis (MBMA) for the same class of drugs. The model-simulated UGE,ss in patients with T2DM was validated by data from the clinical Phase 1e study. Finally, at the end of the Phase 1 study, we simulated the 24-week hemoglobin A1c (HbA1c) level in patients with T2DM of janagliflozin based on the quantitative UGE/FPG/HbA1c relationship informed by our previous MBMA study for the same class of drugs.

RESULTS

The pharmacologically active dose (PAD) levels of multiple ascending dosing (MAD) study were estimated to be 25, 50,100 mg once daily (QD) for 14 days based on the effective PD target of approximately 50 g daily UGE in healthy subjects. Besides, our previous MBMA analysis for the same class of drugs has provided a unified effective PD target of ΔUGEc approximately 0.5-0.6 g/(mg/dL) in both healthy subjects and patients with T2DM. In this study, the model-simulated steady-state ΔUGEc (ΔUGEc,ss) of janagliflozin in patients with T2DM were 0.52, 0.61 and 0.66 g/(mg/dL) for 25, 50, 100 mg QD dose levels. Finally, we estimated that HbA1c at 24 weeks would decrease 0.78 and 0.93 from baseline for the 25 and 50 mg QD dose groups.

CONCLUSIONS

The application of MIDD strategy adequately supported the decision making at each stage of janagliflozin development process. A waiver of Phase 2 study was successfully approved for janagliflozin based on these model-informed results and suggestions. This MIDD strategy of janagliflozin could be further utilized to support the clinical development of other SGLT2 inhibitors.

Clinical significance of the rivaroxaban-dronedarone interaction: insights from physiologically based pharmacokinetic modelling

Patients with atrial fibrillation may require rhythm control therapy in addition to anticoagulation therapy for the prevention of stroke. Since 2012, the European Society of Cardiology and European Heart Rhythm Association guidelines have recommended non-vitamin K antagonist oral anticoagulants, including rivaroxaban, for the prevention of stroke in patients with atrial fibrillation. During the same period, these guidelines have also recommended dronedarone or amiodarone as second-line rhythm control agents in certain patients with atrial fibrillation and no contraindications. Amiodarone and dronedarone both strongly inhibit P-glycoprotein, while dronedarone is a moderate and amiodarone a weak inhibitor of cytochrome P450 3A4 (CYP3A4). Based on these data and evidence from physiologically based pharmacokinetic modelling, amiodarone and dronedarone are expected to have similar effects on rivaroxaban exposure resulting from P-glycoprotein and CYP3A4 inhibition. However, the rivaroxaban label recommends against the concomitant use of dronedarone, but not amiodarone, citing a lack of evidence on the concomitant use of rivaroxaban and dronedarone as the reason for the different recommendations. In this report, we discuss evidence from clinical studies and physiologically based pharmacokinetic modelling on the potential for increased rivaroxaban exposure resulting from drug-drug interaction between rivaroxaban and dronedarone or amiodarone. The current evidence supports the same clinical status and concomitant use of either amiodarone or dronedarone with rivaroxaban, which could be considered in future recommendations.

Physiologically Based Pharmacokinetic Modeling for Quantitative Prediction of Exposure to a Human Disproportionate Metabolite of the Selective NaV1.7 Inhibitor DS-1971a, a Mixed Substrate of Cytochrome P450 and Aldehyde Oxidase, Using Chimeric Mice With Humanized Liver

In a previous study on the human mass balance of DS-1971a, a selective Na_V1.7 inhibitor, its CYP2C8-dependent metabolite M1 was identified as a human disproportionate metabolite. The present study assessed the usefulness of pharmacokinetic evaluation in chimeric mice grafted with human hepatocytes (PXB-mice) and physiologically based pharmacokinetic (PBPK) simulation of M1. After oral administration of radiolabeled DS-1971a, the most abundant metabolite in the plasma, urine, and feces of PXB-mice was M1, while those of control SCID mice were aldehyde oxidase-related metabolites including M4, suggesting a drastic difference in the metabolism between these mouse strains. From a qualitative perspective, the metabolite profile observed in PXB-mice was remarkably similar to that in humans, but the quantitative evaluation indicated that the area under the plasma concentration-time curve (AUC) ratio of M1 to DS-1971a (M1/P ratio) was approximately only half of that in humans. A PXB-mouse-derived PBPK model was then constructed to achieve a more accurate prediction, giving an M1/P ratio (1.3) closer to that in humans (1.6) than the observed value in PXB-mice (0.69). In addition, simulated maximum plasma concentration and AUC values of M1 (3429 ng/ml and 17,116 ng·h/ml, respectively) were similar to those in humans (3180 ng/ml and 18,400 ng·h/ml, respectively). These results suggest that PBPK modeling incorporating pharmacokinetic parameters obtained with PXB-mice is useful for quantitatively predicting exposure to human disproportionate metabolites. SIGNIFICANCE STATEMENT: The quantitative prediction of human disproportionate metabolites remains challenging. This paper reports on a successful case study on the practical estimation of exposure (C _max and AUC) to DS-1971a and its CYP2C8-dependent, human disproportionate metabolite M1, by PBPK simulation utilizing pharmacokinetic parameters obtained from PXB-mice and in vitro kinetics in human liver fractions. This work adds to the growing knowledge regarding metabolite exposure estimation by static and dynamic models.

Accelerating Digitalization in Healthcare with the InSilicoTrials Cloud-Based Platform: Four Use Cases

The use of in silico trials is expected to play an increasingly important role in the development and regulatory evaluation of new medical products. Among the advantages that in silico approaches offer, is that they permit testing of drug candidates and new medical devices using virtual patients or computational emulations of preclinical experiments, allowing to refine, reduce or even replace time-consuming and costly benchtop/in vitro/ex vivo experiments as well as the involvement of animals and humans in in vivo studies. To facilitate and widen the adoption of in silico trials, InSilicoTrials Technologies has developed a cloud-based platform, hosting healthcare simulation tools for different bench, preclinical and clinical evaluations, and for diverse disease areas. This paper discusses four use cases of in silico trials performed using the InSilicoTrials.com platform. The first application illustrates how in silico approaches can improve the early preclinical assessment of drug-induced cardiotoxicity risks. The second use case is a virtual reproduction of a bench test for the safety assessment of transcatheter heart valve substitutes. The third and fourth use cases are examples of virtual patients generation to evaluate treatment effects in multiple sclerosis and prostate cancer patients, respectively.

Pharmacokinetics and pharmacodynamics of anti-tuberculosis drugs: An evaluation of in vitro, in vivo methodologies and human studies

There has been an increased interest in pharmacokinetics and pharmacodynamics (PKPD) of anti-tuberculosis drugs. A better understanding of the relationship between drug exposure, antimicrobial kill and acquired drug resistance is essential not only to optimize current treatment regimens but also to design appropriately dosed regimens with new anti-tuberculosis drugs. Although the interest in PKPD has resulted in an increased number of studies, the actual bench-to-bedside translation is somewhat limited. One of the reasons could be differences in methodologies and outcome assessments that makes it difficult to compare the studies. In this paper we summarize most relevant in vitro, in vivo, in silico and human PKPD studies performed to optimize the drug dose and regimens for treatment of tuberculosis. The in vitro assessment focuses on MIC determination, static time-kill kinetics, and dynamic hollow fibre infection models to investigate acquisition of resistance and killing of Mycobacterium tuberculosis populations in various metabolic states. The in vivo assessment focuses on the various animal models, routes of infection, PK at the site of infection, PD read-outs, biomarkers and differences in treatment outcome evaluation (relapse and death). For human PKPD we focus on early bactericidal activity studies and inclusion of PK and therapeutic drug monitoring in clinical trials. Modelling and simulation approaches that are used to evaluate and link the different data types will be discussed. We also describe the concept of different studies, study design, importance of uniform reporting including microbiological and clinical outcome assessments, and modelling approaches. We aim to encourage researchers to consider methods of assessing and reporting PKPD of anti-tuberculosis drugs when designing studies. This will improve appropriate comparison between studies and accelerate the progress in the field.

Bayesian forecasting of tumor size metrics and overall survival

The tumor size ratio (TSR), time-to-tumor growth (TTG), and tumor growth rate (kG) are frequently suggested as model-based predictors of overall survival (OS) for different types of tumors. When the tumor metrics are applied in forecasting of the outcome for individual patients at an early stage, the tumor data might be sparse resulting in imprecise prediction. This simulation study aimed to investigate how the tumor follow-up data and estimation approaches influence the accuracy in the tumor size metrics and the predicted hazard of death for individual patients. Longitudinal tumor size and OS data were simulated using tumor growth inhibition and Weibull distribution models, respectively. Based on the model and increasing measurement durations, the accuracy (defined as 80-125% of the simulated "true" value) in individual metrics and hazard was computed. TSR week 6 (TSRw6) accuracy was adequate for 91% of the patients when tumor size was measured up to 12 weeks. For TTG and kG metrics, the highest accuracy observed was lower (43 and 77%, respectively) and occurred later (42 and 60 weeks, respectively). The simultaneous (joint) and sequential estimation approaches resulted in similar accuracies, however, in general, the sequential approach where individual tumor size parameters are fixed, demonstrated inferior estimation properties. The TSRw6 and the model-predicted tumor time course (absolute or relative change) had better forecasting properties than TTG or kG. The population pharmacokinetic (PK) parameters and data approach performed similarly or better than the simultaneous approach and had a better accuracy in estimating individuals' hazard of death than the individual PK parameters method.

A case-study of model-informed drug development of a novel PCSK9 anti sense oligonucleotide. Part 1: First time in man to phase II

Here, we show model-informed drug development (MIDD) of a novel antisense oligonucleotide, targeting PCSK9 for treatment of hypocholesteremia. The case study exemplifies use of MIDD to analyze emerging data from an ongoing first-in-human study, utility of the US Food and Drug Administration MIDD pilot program to accelerate timelines, innovative use of competitor data to set biomarker targets, and use of MIDD to optimize sample size and dose selection, as well as to accelerate and de-risk a phase IIb study. The focus of the case-study is on the cross-functional collaboration and other key MIDD enablers that are critical to maximize the value of MIDD, rather than the technical application of MIDD.

Population Pharmacokinetics of Oral Brepocitinib in Healthy Volunteers and Patients

Brepocitinib is a tyrosine kinase 2 and Janus kinase 1 inhibitor in development for treatment of inflammatory autoimmune diseases. This analysis aimed to add to the pharmacokinetic knowledge of the medication, through development of a population pharmacokinetic model and identification of factors that affect drug disposition. Plasma samples from 5 clinical trials were collated, composed of healthy volunteers, patients with psoriasis and patients with alopecia areata taking oral brepocitinib. NONMEM was used to develop a population pharmacokinetic model, and patient demographics were tested as covariates. The final model was a 1-compartment model with first-order absorption. The typical values for apparent clearance and apparent volume of distribution were 18.7 L/h (78% coefficient of variation [CV]) and 136 L (60.5% CV), respectively. Absorption was rapid with an absorption constant of 3.46 h, with an absorption lag of 0.24 hours observed with the oral tablet formulation. The proportional residual error was found to be 52.7% CV in healthy volunteers and 87.5% CV in patients. High-fat meals were associated with a reduction in both the rate (69.9% lower) and extent (28.3% lower) of absorption, while Asian populations had reduced clearance (24.3% lower). Nonlinear pharmacokinetics were observed at doses of 175 mg and above, with a 35.1% higher relative bioavailability at these doses. There were insufficient data to describe this nonlinearity as a continuous relationship. This initial description of the population pharmacokinetics will act as a foundation for the model-informed drug development of brepocitinib and will facilitate future modeling of this medicine. ClinicalTrials.gov numbers NCT02310750 NCT03236493 NCT03656952 NCT02969018 NCT02974868.

Quality Assurance of PBPK Modeling Platforms and Guidance on Building, Evaluating, Verifying and Applying PBPK Models Prudently under the Umbrella of Qualification: Why, When, What, How and By Whom?

Modeling and simulation emerges as a fundamental asset of drug development. Mechanistic modeling builds upon its strength to integrate various data to represent a detailed structural knowledge of a physiological and biological system and is capable of informing numerous drug development and regulatory decisions via extrapolations outside clinically studied scenarios. Herein, physiologically based pharmacokinetic (PBPK) modeling is the fastest growing branch, and its use for particular applications is already expected or explicitly recommended by regulatory agencies. Therefore, appropriate applications of PBPK necessitates trust in the predictive capability of the tool, the underlying software platform, and related models. That has triggered a discussion on concepts of ensuring credibility of model-based derived conclusions. Questions like 'why', 'when', 'what', 'how' and 'by whom' remain open. We seek for harmonization of recent ideas, perceptions, and related terminology. First, we provide an overview on quality assurance of PBPK platforms with the two following concepts. Platform validation: ensuring software integrity, security, traceability, correctness of mathematical models and accuracy of algorithms. Platform qualification: demonstrating the predictive capability of a PBPK platform within a particular context of use. Second, we provide guidance on executing dedicated PBPK studies. A step-by-step framework focuses on the definition of the question of interest, the context of use, the assessment of impact and risk, the definition of the modeling strategy, the evaluation of the platform, performing model development including model building, evaluation and verification, the evaluation of applicability to address the question, and the model application under the umbrella of a qualified platform.

Model-Informed Drug Development of New Cefoperazone Sodium and Sulbactam Sodium Combination (3:1): Pharmacokinetic/Pharmacodynamic Analysis and Antibacterial Efficacy Against Enterobacteriaceae

Objective: Cefoperazone/sulbactam is a commonly used antibiotic combination against the extended-spectrum beta-lactamases (ESBLs)-producing bacteria. The objective of this study was to evaluate the efficacy of a new cefoperazone/sulbactam combination (3:1) for Enterobacteriaceae infection via model-informed drug development (MIDD) approaches.

Methods: Sulperazon [cefoperazone/sulbactam (2:1)] was used as a control. Pharmacokinetic (PK) data was collected from a clinical phase I trial. Minimum inhibitory concentrations (MICs) were determined using two-fold broth microdilution method. The percent time that the free drug concentration exceeded the minimum inhibitory concentration (%fT_>MIC) was used as the pharmacokinetic/pharmacodynamic indicator correlated with efficacy. Models were developed to characterize the PK profile of cefoperazone and sulbactam. Monte Carlo simulations were employed to determine the investigational regimens of cefoperazone/sulbactam (3:1) for the treatment of infections caused by Enterobacteriaceae based on the probability of target attainment (PTA) against the tested bacteria.

Results: Two 2-compartment models were developed to describe the PK profiles of cefoperazone and sulbactam. Simulation results following the single-dose showed that the regimens of cefoperazone/sulbactam combinations in the ratios of 3:1 and 2:1 achieved similar PTA against the tested bacteria. Simulation results from the multiple-dose showed that the dosing regimen of cefoperazone/sulbactam (4 g, TID, 3 g:1 g) showed slightly better antibacterial effect than cefoperazone/sulbactam (6 g, BID, 4 g:2 g) against the Escherichia coli (ESBL⁻) and Klebsiella pneumoniae (ESBL⁻). For the other tested bacteria, the above regimens achieved a similar PTA.

Conclusions: Cefoperazone/sulbactam (3:1) showed similar bactericidal activity to sulperazon [cefoperazone/sulbactam (2:1)] against the tested bacteria. For the ESBL-producing and cefoperazone-resistant E. coli and K. pneumoniae, Cefoperazone/sulbactam (3:1) did not exhibit advantage as anticipated. Our study indicated that further clinical trials should be carried out cautiously to avoid the potential risks of not achieving the expected target.

Quantitative system pharmacology as a legitimate approach to examine extrapolation strategies used to support pediatric drug development

Extrapolation strategies from adult data for designing pediatric drug development programs are explored using the quantitative systems pharmacology (QSP) modeling approach, a mechanistic drug and disease modeling framework that can predict clinical response and guide pediatric drug development in general. This innovative model‐informed drug discovery and development approach can leverage adult‐pediatric pharmacology and disease similarity metrics to validate extrapolation assumptions. We describe the QSP model strategy and framework for extrapolation to design pediatric drug development programs by leveraging adult data across a wide range of therapeutic areas and illustrating stage‐gate decisions informed by such an approach.

Pharmacokinetics and Tissue Distribution of Enavogliflozin in Mice and Rats

This study investigated the pharmacokinetics and tissue distribution of enavogliflozin, a novel sodium-glucose cotransporter 2 inhibitor that is currently in phase three clinical trials. Enavogliflozin showed dose-proportional pharmacokinetics following intravenous and oral administration (doses of 0.3, 1, and 3 mg/kg) in both mice and rats. Oral bioavailability was 84.5–97.2% for mice and 56.3–62.1% for rats. Recovery of enavogliflozin as parent form from feces and urine was 39.3 ± 3.5% and 6.6 ± 0.7%, respectively, 72 h after its intravenous injection (1 mg/kg), suggesting higher biliary than urinary excretion in mice. Major biliary excretion was also suggested for rats, with 15.9 ± 5.9% in fecal recovery and 0.7 ± 0.2% in urinary recovery for 72 h, following intravenous injection (1 mg/kg). Enavogliflozin was highly distributed to the kidney, which was evidenced by the AUC ratio of kidney to plasma (i.e., 41.9 ± 7.7 in mice following its oral administration of 1 mg/kg) and showed slow elimination from the kidney (i.e., T_1/2 of 29 h). It was also substantially distributed to the liver, stomach, and small and large intestine. In addition, the tissue distribution of enavogliflozin after single oral administration was not significantly altered by repeated oral administration for 7 days or 14 days. Overall, enavogliflozin displayed linear pharmacokinetics following intravenous and oral administration, significant kidney distribution, and favorable biliary excretion, but it was not accumulated in the plasma and major distributed tissues, following repeated oral administration for 2 weeks. These features may be beneficial for drug efficacy. However, species differences between rats and mice in metabolism and oral bioavailability should be considered as drug development continues.

Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

Application of quantitative protein mass spectrometric data in the early predictive analysis of target engagement by monoclonal antibodies

Model‐informed drug discovery is endorsed by the US Food and Drug Administration (FDA) to improve the flow of medicines from bench to bedside. In the case of monoclonal antibodies, this necessitates taking into account not only the pharmacokinetic (PK) properties of the drug, but also the tissue distribution, concentration, and turnover of the target to guide dose and affinity selection, as well as serve as a link to downstream pharmacology. Relevant information (e.g., tissue proteomic data from quantitative mass spectrometry), is increasingly available from public domain data repositories, although not necessarily in the form that is directly usable for the purpose of quantitative, predictive, and mechanistic PK/pharmacodynamic (PD) modeling based on molarity or similar frameworks instead. Using secreted plasma protein concentrations measured both by immunochemical methods and mass spectrometry, we addressed this gap and derived an optimized nonlinear empirical function that establishes the correlation between the two data sets and validated the approach taken using a wider data set of all proteins found in plasma. In addition, we present a semimechanistic framework for the plasma half‐life of soluble proteins where clearance is expressed as a nonlinear function of the molecular weight of the protein. Finally, we apply the approach to two established therapeutic antibody targets: complement factor C5 and PCSK9 to demonstrate how the described framework can be applied to predictive PK/PD modeling.

Fixed dosing of kukoamine B in sepsis patients: Results from population pharmacokinetic modelling and simulation

AIMS

To assess the appropriateness of the bodyweight or fixed dosing regimen, population pharmacokinetic (PopPK) model of kukoamine B has been built in sepsis patients.

METHODS

Plasma Concentrations of kukoamine B and the covariates information were from 30 sepsis patients assigned into 0.06 mg/kg, 0.12 mg/kg and 0.24 mg/kg groups in Phase IIa clinical trial. PopPK model was built using nonlinear mixed-effect (NLME) modelling approach. Based on the final model, PK profiles were respectively simulated for 500 times applying the bodyweight and renal function information of 12 sepsis patients from 0.24 mg/kg group on the bodyweight or the fixed dosing regimen. For each dosing regimen, PK profiles of 6000 virtual patients were obtained. Statistical analyses for C_max and C_min were performed. If the biases of C_max and C_min can all meet the criteria of ±15%, the fixed dosing regimen can substitute the bodyweight dosing regimen.

RESULTS

PopPK model was successfully developed by NLME approach. Bi-compartmental model was selected as the basic model. Renal function was identified as a statistically significant covariate about systemic clearance with OFV decreasing 8.6, resulting in a 5.2% decrease inter-individual variability (IIV) of systemic clearance. Body weight was not identified as a statistically significant covariate. Simulation results demonstrated two methods had a bias of 8.1% for C_max , and 8.6% for C_min . Furthermore, PK variability was lower on the fixed dosing regimen than the body weight regimen.

CONCLUSIONS

Based on simulation results, fixed dosing regimen was recommended in the following clinical trials.

In Vitro–In Silico Modeling of Caffeine and Diclofenac Permeation in Static and Fluidic Systems with a 16HBE Lung Cell Barrier

Static in vitro permeation experiments are commonly used to gain insights into the permeation properties of drug substances but exhibit limitations due to missing physiologic cell stimuli. Thus, fluidic systems integrating stimuli, such as physicochemical fluxes, have been developed. However, as fluidic in vitro studies display higher complexity compared to static systems, analysis of experimental readouts is challenging. Here, the integration of in silico tools holds the potential to evaluate fluidic experiments and to investigate specific simulation scenarios. This study aimed to develop in silico models that describe and predict the permeation and disposition of two model substances in a static and fluidic in vitro system. For this, in vitro permeation studies with a 16HBE cellular barrier under both static and fluidic conditions were performed over 72 h. In silico models were implemented and employed to describe and predict concentration–time profiles of caffeine and diclofenac in various experimental setups. For both substances, in silico modeling identified reduced apparent permeabilities in the fluidic compared to the static cellular setting. The developed in vitro–in silico modeling framework can be expanded further, integrating additional cell tissues in the fluidic system, and can be employed in future studies to model pharmacokinetic and pharmacodynamic drug behavior.