Interpretation of Non-Clinical Data for Prediction of Human Pharmacokinetic Parameters: In Vitro-In Vivo Extrapolation and Allometric Scaling

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction

The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting that approximately 90% of drugs fail to make it through the process. The identification of unexpected toxicity issues during preclinical stages is a significant factor contributing to this high rate of failure. These issues can have a major impact on the success of a drug and must be carefully considered throughout the development process. These late-stage rejections or withdrawals of drug candidates significantly increase the costs associated with drug development, particularly when toxicity is detected during clinical trials or after market release. Understanding drug-biological target interactions is essential for evaluating compound toxicity and safety, as well as predicting therapeutic effects and potential off-target effects that could lead to toxicity. This will enable scientists to predict and assess the safety profiles of drug candidates more accurately. Evaluation of toxicity and safety is a critical aspect of drug development, and biomolecules, particularly proteins, play vital roles in complex biological networks and often serve as targets for various chemicals. Therefore, a better understanding of these interactions is crucial for the advancement of drug development. The development of computational methods for evaluating protein-ligand interactions and predicting toxicity is emerging as a promising approach that adheres to the 3Rs principles (replace, reduce, and refine) and has garnered significant attention in recent years. In this review, we present a thorough examination of the latest breakthroughs in drug toxicity prediction, highlighting the significance of drug-target binding affinity in anticipating and mitigating possible adverse effects. In doing so, we aim to contribute to the development of more effective and secure drugs.

Untangling the mess of CGRP levels as a migraine biomarker: an in-depth literature review and analysis of our experimental experience

BACKGROUND

Calcitonin gene-related peptide (CGRP) is the most promising candidate to become the first migraine biomarker. However, literature shows clashing results and suggests a methodological source for such discrepancies. We aimed to investigate some of these methodological factors to evaluate the actual role of CGRP as biomarker.

METHODS

Previous to the experimental part, we performed a literature review of articles measuring CGRP in migraine patients. Using our 399 bio-bank sera samples, we performed a series of experiments to test the validity of different ELISA kits employed, time of sample processing, long-term storage, sampling in rest or after moderate exercise. Analysis of in-house data was performed to analyse average levels of the peptide and the effect of sex and age.

RESULTS

Literature review shows the high variability in terms of study design, determination methods, results and conclusions obtained by studies including CGRP determinations in migraine patients. CGRP measurements depends on the method and specific kit employed, also on the isoform detected, showing completely different ranges of concentrations. Alpha-CGRP and beta-CGRP had median with IQR levels of 37.5 (28.2-54.4) and 4.6 (2.4-6.4)pg/mL, respectively. CGRP content is preserved in serum within the 24 first hours when samples are stored at 4°C after clotting and immediate centrifugation. Storages at -80°C of more than 6 months result in a decrease in CGRP levels. Moderate exercise prior to blood extraction does not modulate the concentration of the peptide. Age positively correlates with beta-CGRP content and men have higher alpha-CGRP levels than women.

CONCLUSIONS

We present valuable information for CGRP measurements in serum. ELISA kit suitability should be tested prior to the experiments. Alpha and beta-CGRP levels should be analysed separately as they can show different behaviours even within the same condition. Samples can be processed in a 24-h window if they have been kept in 4°C and should not be stored for more than 6 months at -80°C before assayed. Patients do not need to rest before the blood extraction unless they have performed a high-endurance exercise. For comparative studies, sex and age should be accounted for as these parameters can impact CGRP concentrations.

Combined translational pharmacometrics approach to support the design and conduct of the first-in-human study of DWP16001

AIMS

The objective of this study was to characterize the pharmacokinetics (PK)/pharmacodynamics (PD) of DWP16001, a novel sodium-glucose cotransporter 2 inhibitor, and predict efficacious doses for the first-in-human study using various translational approaches.

METHODS

A mechanistic PK/PD model was developed for DWP16001 using nonlinear mixed-effect modelling to describe animal PK/PD properties. Using allometry and in silico physiologically based equations, human PK parameters were predicted. Human PD parameters were scaled by applying interspecies difference and in vitro drug-specific factors. Human parameters were refined using early clinical data. Model-predicted PK and PD outcomes were compared to observations before and after parameter refinement.

RESULTS

The PK/PD model of DWP16001 was developed using a 2-compartment model with first-order absorption and indirect response. Efficacious doses of 0.3 and 2 mg of DWP16001 were predicted using human half-maximal inhibitory concentration values translated from Zucker Diabetic Fatty rats and normal rats, respectively. After parameter refinement, doses of 0.2 and 1 mg were predicted to be efficacious for each disease model, which improved the prediction results to within a 1.2-fold difference between the model prediction and observation.

CONCLUSIONS

This study predicted efficacious human doses of DWP16001 using population PK/PD modelling and a combined translational pharmacometrics approach. Early clinical data allowed the methods used to translate in vitro and in vivo findings to clinical PK/PD values for DWP16001 to be optimized. This study has shown that a refinement step can be readily applied to improve model prediction and further support the study design and conduct of a first-in-human study.

Development and Evaluation of Amorphous Solid Dispersion of Riluzole with PBPK Model to Simulate the Pharmacokinetic Profile

In the current work, screening of polymers viz. polyacrylic acid (PAA), polyvinyl pyrrolidone vinyl acetate (PVP VA), and hydroxypropyl methyl cellulose acetate succinate (HPMC AS) based on drug-polymer interaction and wetting property was done for the production of a stable amorphous solid dispersion (ASD) of a poorly water-soluble drug Riluzole (RLZ). PAA showed maximum interaction and wetting property hence, was selected for further studies. Solid state characterization studies confirmed the formation of ASD with PAA. Saturation solubility, dissolution profile, and in vivo pharmacokinetic data of the ASD formulation were generated in rats against its marketed tablet Rilutor. The RLZ:PAA ASD showed exponential enhancement in the dissolution of RLZ. Predicted and observed pharmacokinetic data in rats showed enhanced area under curve (AUC) and Cmax in plasma and brain with respect to Rilutor. Furthermore, a physiologically based pharmacokinetic (PBPK) model of rats for Rilutor and RLZ ASD was developed and then extrapolated to humans where physiological parameters were changed along with a biochemical parameter. The partition coefficient was kept similar in both species. The model was used to predict different exposure scenarios, and the simulated data was compared with observed data points. The PBPK model simulated Cmax and AUC was within two times the experimental data for plasma and brain. The Cmax and AUC in the brain increased with ASD compared to Rilutor for humans showing its potential in improving its biopharmaceutical performance and hence enhanced therapeutic efficacy. The model can predict the RLZ concentration in multiple compartments including plasma and liver.

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

Identification of GDC-1971 (RLY-1971), a SHP2 Inhibitor Designed for the Treatment of Solid Tumors

Protein tyrosine phosphatase SHP2 mediates RAS-driven MAPK signaling and has emerged in recent years as a target of interest in oncology, both for treating with a single agent and in combination with a KRAS inhibitor. We were drawn to the pharmacological potential of SHP2 inhibition, especially following the initial observation that drug-like compounds could bind an allosteric site and enforce a closed, inactive state of the enzyme. Here, we describe the identification and characterization of GDC-1971 (formerly RLY-1971), a SHP2 inhibitor currently in clinical trials in combination with KRAS G12C inhibitor divarasib (GDC-6036) for the treatment of solid tumors driven by a KRAS G12C mutation.

Vinblastine pharmacokinetics in mouse, dog, and human in the context of a physiologically based model incorporating tissue-specific drug binding, transport, and metabolism

Vinblastine (VBL) is a vinca alkaloid-class cytotoxic chemotherapeutic that causes microtubule disruption and is typically used to treat hematologic malignancies. VBL is characterized by a narrow therapeutic index, with key dose-limiting toxicities being myelosuppression and neurotoxicity. Pharmacokinetics (PK) of VBL is primarily driven by ABCB1-mediated efflux and CYP3A4 metabolism, creating potential for drug-drug interaction. To characterize sources of variability in VBL PK, we developed a physiologically based pharmacokinetic (PBPK) model in Mdr1a/b(-/-) knockout and wild-type mice by incorporating key drivers of PK, including ABCB1 efflux, CYP3A4 metabolism, and tissue-specific tubulin binding, and scaled this model to accurately simulate VBL PK in humans and pet dogs. To investigate the capability of the model to capture interindividual variability in clinical data, virtual populations of humans and pet dogs were generated through Monte Carlo simulation of physiologic and biochemical parameters and compared to the clinical PK data. This model provides a foundation for predictive modeling of VBL PK. The base PBPK model can be further improved with supplemental experimental data identifying drug-drug interactions, ABCB1 polymorphisms and expression, and other sources of physiologic or metabolic variability.

Approaches for estimating the clinical starting dose of new dosage forms: An example of a long-acting injectable formulation of finasteride

In our previous study, a long-acting injectable (LAI) formulation of finasteride was prepared as a new dosage form of PROPECIA®, and in vivo pharmacokinetics (PKs)-pharmacodynamics (PDs) was evaluated in beagle dogs. The resulting PK-PD profiles of the formulation showed pharmacological effects and achievability for monthly delivery. In this study, a first-in-human (FIH) dose of the LAI formulation loaded with finasteride was predicted. The three approaches were used for estimating a FIH dose of the LAI formulation: (1) No observed adverse effect level (NOAEL)-based approach; (2) Pharmacokinetically-guided approach; (3) Pharmacokinetic/pharmacodynamic model-based approach. The advantage, assumptions, limitations, and estimated FIH dose from each approach was discussed and compared since there is no consensus on the best approach. For the prediction of clinical exposures and estimation of FIH doses, the clinical PK-PD parameters were allometrically scaled from the nonclinical data, extracted from reported clinical studies, or fixed from published literature. The starting dose range of the LAI formulation (as finasteride) was estimated to be 16.80-81.06 mg from the three approaches, and the PK/PD model-based approach suggests the most optimal starting dose (16.80 mg) of the LAI formulation. The approaches for estimating starting doses presented in the study could be used as a basis for an Investigational New Drug (IND) application of new dosage forms.

Prospective Prediction of Dapaconazole Clinical Drug-Drug Interactions Using an In Vitro to In Vivo Extrapolation Equation and PBPK Modeling

This study predicted dapaconazole clinical drug−drug interactions (DDIs) over the main Cytochrome P450 (CYP) isoenzymes using static (in vitro to in vivo extrapolation equation, IVIVE) and dynamic (PBPK model) approaches. The in vitro inhibition of main CYP450 isoenzymes by dapaconazole in a human liver microsome incubation medium was evaluated. A dapaconazole PBPK model (Simcyp version 20) in dogs was developed and qualified using observed data and was scaled up for humans. Static and dynamic models to predict DDIs following current FDA guidelines were applied. The in vitro dapaconazole inhibition was observed for all isoforms investigated, including CYP1A2 (IC50 of 3.68 µM), CYP2A6 (20.7 µM), 2C8 (104.1 µM), 2C9 (0.22 µM), 2C19 (0.05 µM), 2D6 (0.87 µM), and 3A4 (0.008−0.03 µM). The dynamic (PBPK) and static DDI mechanistic model-based analyses suggest that dapaconazole is a weak inhibitor (AUCR > 1.25 and <2) of CYP1A2 and CYP2C9, a moderate inhibitor (AUCR > 2 and <5) of CYP2C8 and CYP2D6, and a strong inhibitor (AUCR ≥ 5) of CYP2C19 and CYP3A, considering a clinical scenario. The results presented may be a useful guide for future in vivo and clinical dapaconazole studies.

Determination of human FaFg of polyphenols using allometric scaling

Certain polyphenols exhibit low permeability; precise prediction of their intestinal absorption is important for understanding internal exposure in humans. Intestinal availability, which represents the fraction of administered compounds that reach the portal blood (F_aF_g), is calculated by dividing bioavailability (F) by hepatic availability (F_h), and F is obtained from pharmacokinetic data from both intravenous (i.v.) and oral (p.o.) administration. However, human F_aF_g of polyphenols is hardly reported, as human i.v. data are extremely scarce. In this study, we developed an estimation method for F_aF_g of polyphenols in humans based on the extrapolation of rat clearance using allometric scaling (allometric scaling-based F_aF_g calculation method, AS- F_aF_gCM). First, for quercetin, for which human i.v. data have been reported, we compared the F_aF_g obtained by AS-F_aF_gCM with the traditional approach using human i.v. and p.o. data. Less than two-fold difference in FaFg values was observed between the two approaches. Next, we obtained F_aF_g of structurally diverse polyphenols (genistein, baicalein, resveratrol, and epicatechin) using AS-F_aF_gCM, demonstrating that all of them were poorly absorbable. Furthermore, to utilize the pharmacokinetic data of the total concentration, including aglycones and metabolites, we modified the AS-F_aF_gCM to focus on their excretion. The F_aF_g value of naringenin was obtained using modified AS-F_aF_gCM and was nearly equal to that of baicalein, a structural isomer of naringenin. This study provides quantitative information on the intestinal absorption of polyphenols using comprehensive estimation methods.

Metabolic clearance of select opioids and opioid antagonists using hepatic spheroids and recombinant cytochrome P450 enzymes

The opioid crisis is a pressing public health issue, exacerbated by the emergence of more potent synthetic opioids, particularly fentanyl and its analogs. While competitive antagonists exist, their efficacy against synthetic opioids is largely unknown. Furthermore, due to the short durations of action of current antagonists, renarcotization remains a concern. In this study, metabolic activity was characterized for fentanyl‐class opioids and common opioid antagonists using multiple in vitro systems, namely, cytochrome P450 (CYP) enzymes and hepatic spheroids, after which an in vitro‐in vivo correlation was applied to convert in vitro metabolic activity to predictive in vivo intrinsic clearance. For all substrates, intrinsic hepatic metabolism was higher than the composite of CYP activities, due to fundamental differences between whole cells and single enzymatic reactions. Of the CYP isozymes investigated, 3A4 yielded the highest absolute and relative metabolism across all substrates, with largely negligible contributions from 2D6 and 2C19. Comparative analysis highlighted elevated lipophilicity and diminished CYP3A4 activity as potential considerations for the development of more efficacious opioid antagonists. Finally, antagonists with a high degree of molecular similarity exhibited comparable clearance, providing a basis for structure‐metabolism relationships. Together, these results provide multiple screening criteria for early stage drug discovery involving opioid countermeasures.

Towards best use and regulatory acceptance of generic physiologically based kinetic (PBK) models for in vitro-to-in vivo extrapolation (IVIVE) in chemical risk assessment

With an increasing need to incorporate new approach methodologies (NAMs) in chemical risk assessment and the concomitant need to phase out animal testing, the interpretation of in vitro assay readouts for quantitative hazard characterisation becomes more important. Physiologically based kinetic (PBK) models, which simulate the fate of chemicals in tissues of the body, play an essential role in extrapolating in vitro effect concentrations to in vivo bioequivalent exposures. As PBK-based testing approaches evolve, it will become essential to standardise PBK modelling approaches towards a consensus approach that can be used in quantitative in vitro-to-in vivo extrapolation (QIVIVE) studies for regulatory chemical risk assessment based on in vitro assays. Based on results of an ECETOC expert workshop, steps are recommended that can improve regulatory adoption: (1) define context and implementation, taking into consideration model complexity for building fit-for-purpose PBK models, (2) harmonise physiological input parameters and their distribution and define criteria for quality chemical-specific parameters, especially in the absence of in vivo data, (3) apply Good Modelling Practices (GMP) to achieve transparency and design a stepwise approach for PBK model development for risk assessors, (4) evaluate model predictions using alternatives to in vivo PK data including read-across approaches, (5) use case studies to facilitate discussions between modellers and regulators of chemical risk assessment. Proof-of-concepts of generic PBK modelling approaches are published in the scientific literature at an increasing rate. Working on the previously proposed steps is, therefore, needed to gain confidence in PBK modelling approaches for regulatory use.

Brain pharmacokinetics and metabolism of the AMP-activated protein kinase selective inhibitor SBI-0206965, an investigational agent for the treatment of glioblastoma

PURPOSE

Emerging evidence suggests that 5' Adenosine Monophosphate-Activated Protein Kinase (AMPK), a key regulator of cellular bioenergetics, is a novel target for the treatment of glioblastoma (GBM), a lethal brain tumor. SBI-0206965, an aminopyrimidine derivative, is a potent AMPK inhibitor being investigated for the treatment of GBM. Here we characterized the systemic and brain pharmacokinetics (PK) and hepatic metabolism of SBI-0206965.

METHODS

We performed intracerebral microdialysis to determine brain partitioning of SBI-0206965 in jugular vein cannulated rats. We assessed systemic PK of SBI-0206965 in rats and C57BL/6 mice following oral administration. Employing human, mouse, and rat liver microsomes we characterized the metabolism of SBI-0206965.

RESULTS

SBI-0206965 is quickly absorbed, achieving plasma and brain extracellular fluid (ECF) peak levels within 0.25 - 0.65 h. Based on the ratio of C_max and AUC in brain ECF to plasma (corrected for protein binding), brain partitioning is ~ 0.6-0.9 in rats. However, the compound has a short elimination half-life (1-2 h) and low relative oral bioavailability (~ 0.15). The estimated in-vitro hepatic intrinsic clearance of SBI-0206965 in mouse, rat and human was 325, 76 and 68 mL/min/kg, respectively. SBI-0206965 metabolites included desmethylated products, and the metabolism was strongly inhibited by ketoconazole, a CYP3A inhibitor.

CONCLUSION

SBI-0206965 has adequate brain permeability but low relative oral bioavailability which may be due to rapid hepatic metabolism, likely catalyzed by CYP3A enzymes. Our observations will facilitate further development of SBI-0206965, and/or other structurally related molecules, for the treatment of GBM and other brain tumors.

Human risk assessment of 4-n-nonylphenol (4-n-NP) using physiologically based pharmacokinetic (PBPK) modeling: analysis of gender exposure differences and application to exposure analysis related to large exposure variability in population

As a toxic substance, 4-n-nonylphenol (4-n-NP) or 4-nonylphenol (4-NP) is widely present in the environment. 4-n-NP is a single substance with a linear-alkyl side chain, but 4-NP usually refers to a random mixture containing various branched types. Unfortunately, human risk assessment and/or exposure level analysis for 4-n-NP (or 4-NP) were almost nonexistent, and related research was urgently needed. This study aimed to analyze the various exposures of 4-n-NP (or 4-NP) through development of a physiologically based-pharmacokinetic (PBPK) model considering gender difference in pharmacokinetics of 4-n-NP and its application to human risk assessment studies. A PBPK model was newly developed considering gender differences in 4-n-NP pharmacokinetics and applied to a human risk assessment for each gender. Exposure analysis was performed using a PBPK model that considered gender differences in 4-n-NP (or 4-NP) exposure and high variabilities in several countries. Furthermore, an extended application was attempted as a human risk assessment for random mixture 4-NP, which is difficult to accurately evaluate in reality. External-exposure and margin-of-safety estimated with the same internal exposure amount differed between genders, meaning the need for a differentiated risk assessment considering gender. Exposure analysis based on biomonitoring data confirmed large variability in exposure to 4-n-NP (or 4-NP) by country, group, and period. External-exposures estimated using PBPK model varied widely, ranging from 0.039 to 63.875 mg/kg/day (for 4-n-NP or 4-NP). By country, 4-n-NP (or 4-NP) exposure was higher in females than in males and the margin-of-safety tended to be low. Overall, exposure to 4-n-NP (or 4-NP) in populations was largely not safe, suggesting need for ongoing management and monitoring. Considering low in vivo accumulation confirmed by PBPK model, risk reduction of 4-n-NP is possible by reducing its use.

DallphinAtoM: Physiologically based pharmacokinetics software predicting human PK parameters based on physicochemical properties, in vitro and animal in vivo data

BACKGROUND AND OBJECTIVES

In silico experiments and simulations using physiologically based pharmacokinetic (PBPK) and allometric approaches have played an important role in pharmaceutical research and drug development. These methods integrate diverse data from preclinical and clinical development, and have been widely applied to in vitro-in vivo extrapolation (IVIVE) of absorption, distribution, metabolism, and excretion (ADME).

METHODS

To develop a user-friendly open tool predicting human PK, we assessed various references on PBPK and allometric methods published so far. They were integrated into a software system named "DallphinAtoM" (Drugs with ALLometry and PHysiology Inside-Animal to huMan), which has a user-friendly platform that can handle complex PBPK models and allometric models with a relatively small amount of essential information of the drug. The models of DallphinAtoM support the integration of data gained during the nonclinical development phase, enable translation from animal to human, and allow the prediction of concentration-time profiles with predicted PK parameters.

RESULTS

We presented two illustrative applications using DallphinAtoM: (1) human PK simulation of an orally administered drug using PBPK method; and (2) simulation of intravenous infusion following a two-compartment model using the allometric scaling method.

CONCLUSIONS

We conclude that this is a straightforward and transparent tool allowing fast and reliable human PK simulation based on the latest knowledge on biochemical processes and physiology and provides valuable information for decision making during the early-phase drug development.

Discovery of SHR5133, a Highly Potent and Novel HBV Capsid Assembly Modulator

Capsid assembly modulators (CpAMs) represent a new class of antivirals targeting hepatitis B virus (HBV) core protein to disrupt the assembly process. In this work, a novel chemotype featuring a fused heterocycle amide was discovered through pharmacophore exploration. Lead optimization resulted in compound 8 with an EC₅₀ value of 511 nM, and then methyl substitution on the piperazine was found to improve the in vitro potency remarkably. Further SAR studies established the key compound SHR5133, which showed high in vitro antiviral potency, favorable pharmacokinetic profiles across species, and robust in vivo efficacy.

Development and application of a physiologically based pharmacokinetic model for entrectinib in rats and scale-up to humans: Route-dependent gut wall metabolism

Entrectinib (Rozlytrek®) is an oral antineoplastic agent approved by the U.S. Food and Drug Administration in 2019 for the treatment of c-ros oncogene 1 (ROS1)-positive non-small cell lung cancer and neurotrophic tyrosine receptor kinase (NTRK) fusion-positive solid tumors. Although there have been a few studies on the pharmacokinetics of entrectinib, the relative contributions of several kinetic factors determining the oral bioavailability and systemic exposure of entrectinib are still worthy of investigation. Experimental data on the intestinal absorption and disposition of entrectinib in rats were acquired from studies on in vitro protein binding/tissue S9 metabolism, in situ intestinal perfusion, and in vivo dose-escalation/hepatic extraction. Using these datasets, an in-house whole-body physiologically based pharmacokinetic (PBPK) model incorporating the Q_Gut model concepts and segregated blood flow in the gut was constructed and optimized with respect to drug-specific parameters. The established rat PBPK model was further extrapolated to humans through relevant physiological scale-up and parameter optimization processes. The optimized rat and human PBPK models adequately captured the impact of route-dependent gut metabolism on the systemic exposure to entrectinib and closely mirrored various preclinical and clinical observations. Our proposed PBPK model could be useful in optimizing dosage regimens and predicting drug interaction potential in various clinical conditions, after partial modification and validation.

Antiparasitic Effects of Sulfated Polysaccharides from Marine Hydrobionts

This review presents materials characterizing sulfated polysaccharides (SPS) of marine hydrobionts (algae and invertebrates) as potential means for the prevention and treatment of protozoa and helminthiasis. The authors have summarized the literature on the pathogenetic targets of protozoa on the host cells and on the antiparasitic potential of polysaccharides from red, brown and green algae as well as certain marine invertebrates. Information about the mechanisms of action of these unique compounds in diseases caused by protozoa has also been summarized. SPS is distinguished by high antiparasitic activity, good solubility and an almost complete absence of toxicity. In the long term, this allows for the consideration of these compounds as effective and attractive candidates on which to base drugs, biologically active food additives and functional food products with antiparasitic activity.

Long-acting injectable donepezil microspheres: Formulation development and evaluation

Novel formulations of donepezil (DNP)-loaded microspheres based on a bio-degradable polymer of poly(lactic-co-glycolic acid) (PLGA) with a one-month duration of effect were developed, aimed at reducing dosing frequency and adverse effects and improving patient adherence. The spherical and monodispersed DNP-loaded microspheres were precisely fabricated by the Inventage Lab Precision Particle Fabrication method (IVL-PPFM®) based on micro-electromechanical systems (MEMS) and microfluidic technology. The types of polymers and end-groups, the drug/polymer ratio (DPR), and the routes of administration for DNP were studied to ensure an effective concentration and desired duration. Laser-light particle size analysis and scanning electron microscopy were used to characterization. Also, non-clinical animal models of beagle dogs are used to optimize DNP formulations and evaluate their pharmacokinetic properties. The PK results showed that the DPR was a critical factor in determining the exposure level and duration of DNR release. Furthermore, the lactide ratio, which varied depending upon the type of polymer, determined the hydrophobic interaction and was also an important factor affecting the desired DNP release. Since DNP shows a large inter-species variation between dogs and humans, PK modeling and simulation of the reference drug (i.e., Aricept®) and DNP-loaded microspheres were used for formulation development to overcome and interpret these variations. In addition, the developed PK model was extrapolated to humans using the estimated PK parameter and published clinical pharmacology data for DNP. The predicted PK profile of the DNP-loaded microsphere in humans showed that the formulation with PLGA 7525A and the DPR of 1/9 could maintain drug concentration for a month and could control initial burst release. The data obtained from the study could be used as scientific evidence for decision-making in future formulation development.

Prediction of Pregnancy-Induced Changes in Secretory and Total Renal Clearance of Drugs Transported by Organic Anion Transporters

Pregnancy can significantly change the pharmacokinetics of drugs, including those renally secreted by organic anion transporters (OATs). Quantifying these changes in pregnant women is logistically and ethically challenging. Hence, predicting the in vivo plasma renal secretory clearance (CLsec) and renal CL (CLrenal) of OAT drugs in pregnancy is important to design correct dosing regimens of OAT drugs. Here, we first quantified the fold-change in renal OAT activity in pregnant versus nonpregnant individual using available selective OAT probe drug CLrenal data (training dataset; OAT1: tenofovir, OAT2: acyclovir, OAT3: oseltamivir carboxylate). The fold-change in OAT1 activity during the 2nd and 3rd trimester was 2.9 and 1.0 compared with nonpregnant individual, respectively. OAT2 activity increased 3.1-fold during the 3rd trimester. OAT3 activity increased 2.2, 1.7 and 1.3-fold during the 1st, 2nd, and 3rd trimester, respectively. Based on these data, we predicted the CLsec, CLrenal and total clearance ((CLtotal) of drugs in pregnancy, which are secreted by multiple OATs (verification dataset; amoxicillin, pravastatin, cefazolin and ketorolac, R-ketorolac, S-ketorolac). Then, the predicted clearances (CLs) were compared with the observed values. The predicted/observed CLsec, CLrenal, and CLtotal of drugs in pregnancy of all verification drugs were within 0.80-1.25 fold except for CLsec of amoxicillin in the 3rd trimester (0.76-fold) and cefazolin in the 2nd trimester (1.27-fold). Overall, we successfully predicted the CLsec, CLrenal, and CLtotal of drugs in pregnancy that are renally secreted by multiple OATs. This approach could be used in the future to adjust dosing regimens of renally secreted OAT drugs which are administered to pregnant women. SIGNIFICANCE STATEMENT: To the authors' knowledge, this is the first report to successfully predict renal secretory clearance and renal clearance of multiple OAT substrate drugs during pregnancy. The data presented here could be used in the future to adjust dosing regimens of renally secreted OAT drugs in pregnancy. In addition, the mechanistic approach used here could be extended to drugs transported by other renal transporters.

A Biophysical Insight of Camptothecin Biodistribution: Towards a Molecular Understanding of Its Pharmacokinetic Issues

Camptothecin (CPT) is a potent anticancer drug, and its putative oral administration is envisioned although difficult due to physiological barriers that must be overcome. A comprehensive biophysical analysis of CPT interaction with biointerface models can be used to predict some pharmacokinetic issues after oral administration of this or other drugs. To that end, different models were used to mimic the phospholipid composition of normal, cancer, and blood–brain barrier endothelial cell membranes. The logD values obtained indicate that the drug is well distributed across membranes. CPT-membrane interaction studies also confirm the drug’s location at the membrane cooperative and interfacial regions. The drug can also permeate membranes at more ordered phases by altering phospholipid packing. The similar logD values obtained in membrane models mimicking cancer or normal cells imply that CPT has limited selectivity to its target. Furthermore, CPT binds strongly to serum albumin, leaving only 8.05% of free drug available to be distributed to the tissues. The strong interaction with plasma proteins, allied to the large distribution (VD_SS = 5.75 ± 0.932 L·Kg⁻¹) and tendency to bioaccumulate in off-target tissues, were predicted to be pharmacokinetic issues of CPT, implying the need to develop drug delivery systems to improve its biodistribution.

Recent developments in in vitro and in vivo models for improved translation of preclinical pharmacokinetics and pharmacodynamics data

Improved pharmacokinetics/pharmacodynamics (PK/PD) prediction in the early stages of drug development is essential to inform lead optimization strategies and reduce attrition rates. Recently, there have been significant advancements in the development of new in vitro and in vivo strategies to better characterize pharmacokinetic properties and efficacy of drug leads. Herein, we review advances in experimental and mathematical models for clearance predictions, advancements in developing novel tools to capture slowly metabolized drugs, in vivo model developments to capture human etiology for supporting drug development, limitations and gaps in these efforts, and a perspective on the future in the field.

Overcoming the shortcomings of the extended-clearance concept: a framework for developing a physiologically-based pharmacokinetic (PBPK) model to select drug candidates involving transporter-mediated clearance

Introduction:Human pharmacokinetic (PK) prediction can be a significant challenge to drug candidates undergoing transporter-mediated clearance, when only animal data and in vitro human parameters are available in the drug discovery stage.Areas covered:The extended clearance concept (ECC) that incorporates the processes of hepatic uptake, passive diffusion, metabolism and biliary secretion has been adapted to determine the rate-determining process of hepatic clearance and drug-drug interactions (DDIs). However, since the ECC is derived from the well-stirred model and does not consider the liver as a drug distribution organ to reflect the time-dependent variation of drug concentrations between the liver and plasma, it can be misused for compound selection in drug discovery.Expert opinion:The PBPK model consists of a set of differential equations of drug mass balance, and can overcome the shortcomings of the ECC in predicting human PK. The predictability, relevance and reliability of the model and the scaling factors for IVIVE must be validated using either the measured liver concentrations or DDI data with known transporter inhibitors, or both, in monkeys. A human PBPK model that incorporates in vitro human data and SFs obtained from the validated monkey PBPK model can be used for compound selection in the drug discovery phase.

Development and Evaluation of a Physiologically Based Pharmacokinetic Drug-Disease Model of Propranolol for Suggesting Model Informed Dosing in Liver Cirrhosis Patients

AIM

The study was aimed to understand the underlying causes for the differences in propranolol pharmacokinetics (PK) between healthy and cirrhosis populations by using a systematic whole-body physiologically based pharmacokinetic (PBPK) model-building approach for suggesting model informed propranolol dosing in liver cirrhosis patients with different stages of disease severity.

METHODS

A whole-body PBPK model was developed by using population simulator PK-Sim® by using reported physicochemical and clinical data for propranolol in healthy and liver cirrhosis populations. The model evaluation was done by visual verification and comparison of PK parameters using their observed/predicted ratios (Robs/pred).

RESULTS

The developed model has effectively described the disposition of propranolol after intravenous and oral application in healthy and liver cirrhosis populations. All the model predictions were comparable to the observed clinical data and the Robs/pred for all the PK parameters were within a 2-fold range. A significant increase in plasma concentration of propranolol and decrease in drug clearance was observed in progressive stages of liver cirrhosis. The developed model after evaluation with the reported clinical PK data was used for suggesting model informed propranolol dosing in different stages of liver cirrhosis based on systemic unbound drug concentration.

CONCLUSION

The developed PBPK model has successfully described propranolol PK in healthy and cirrhosis populations after IV and oral administration. The evaluated PBPK propranolol-cirrhosis model can have many implications in predicting propranolol dosing in liver cirrhosis patients with different stages of disease severity.

Prediction of Drug Clearance from Enzyme and Transporter Kinetics

Accurate estimation of in vivo clearance in human is pivotal to determine the dose and dosing regimen for drug development. In vitro-in vivo extrapolation (IVIVE) has been performed to predict drug clearance using empirical and physiological scalars. Multiple in vitro systems and mathematical modeling techniques have been employed to estimate in vivo clearance. The models for predicting clearance have significantly improved and have evolved to become more complex by integrating multiple processes such as drug metabolism and transport as well as passive diffusion. This chapter covers the use of conventional as well as recently developed methods to predict metabolic and transporter-mediated clearance along with the advantages and disadvantages of using these methods and the associated experimental considerations. The general approaches to improve IVIVE by use of appropriate scalars, incorporation of extrahepatic metabolism and transport and application of physiologically based pharmacokinetic (PBPK) models with proteomics data are also discussed. The chapter also provides an overview of the advantages of using such dynamic mechanistic models over static models for clearance predictions to improve IVIVE.

Novel testing strategy for prediction of rat biliary excretion of intravenously administered estradiol-17β glucuronide

The aim of the present study was to develop a generic rat physiologically based kinetic (PBK) model that includes a novel testing strategy where active biliary excretion is incorporated using estradiol-17β glucuronide (E217βG) as the model substance. A major challenge was the definition of the scaling factor for the in vitro to in vivo conversion of the PBK-model parameter Vmax. In vitro values for the Vmax and Km for transport of E217βG were found in the literature in four different studies based on experiments with primary rat hepatocytes. The required scaling factor was defined based on fitting the PBK model-based predicted values to reported experimental data on E217βG blood levels and cumulative biliary E217βG excretion. This resulted in a scaling factor of 129 mg protein/g liver. With this scaling factor the PBK model predicted the in vivo data for blood and cumulative biliary E217βG levels with on average of less than 1.8-fold deviation. The study provides a proof of principle on how biliary excretion can be included in a generic PBK model using primary hepatocytes to define the kinetic parameters that describe the biliary excretion.

Predicting Volume of Distribution in Humans: Performance of In Silico Methods for a Large Set of Structurally Diverse Clinical Compounds

Volume of distribution at steady state (V_D,ss) is one of the key pharmacokinetic parameters estimated during the drug discovery process. Despite considerable efforts to predict V_D,ss, accuracy and choice of prediction methods remain a challenge, with evaluations constrained to a small set (<150) of compounds. To address these issues, a series of in silico methods for predicting human V_D,ss directly from structure were evaluated using a large set of clinical compounds. Machine learning (ML) models were built to predict V_D,ss directly and to predict input parameters required for mechanistic and empirical V_D,ss predictions. In addition, log D, fraction unbound in plasma (fup), and blood-to-plasma partition ratio (BPR) were measured on 254 compounds to estimate the impact of measured data on predictive performance of mechanistic models. Furthermore, the impact of novel methodologies such as measuring partition (Kp) in adipocytes and myocytes (n = 189) on V_D,ss predictions was also investigated. In predicting V_D,ss directly from chemical structures, both mechanistic and empirical scaling using a combination of predicted rat and dog V_D,ss demonstrated comparable performance (62%–71% within 3-fold). The direct ML model outperformed other in silico methods (75% within 3-fold, r² = 0.5, AAFE = 2.2) when built from a larger data set. Scaling to human from predicted V_D,ss of either rat or dog yielded poor results (<47% within 3-fold). Measured fup and BPR improved performance of mechanistic V_D,ss predictions significantly (81% within 3-fold, r² = 0.6, AAFE = 2.0). Adipocyte intracellular Kp showed good correlation to the V_D,ss but was limited in estimating the compounds with low V_D,ss.

Incorporating Pharmacological Target-Mediated Drug Disposition (TMDD) in a Whole-Body Physiologically Based Pharmacokinetic (PBPK) Model of Linagliptin in Rat and Scale-up to Human

Linagliptin demonstrates substantial nonlinear pharmacokinetics due to its saturable binding to its pharmacological target dipeptidyl peptide 4 (DPP-4), a phenomenon known as target-mediated drug disposition (TMDD). In the current study, we established a novel whole-body physiologically-based pharmacokinetic (PBPK)-TMDD model for linagliptin. This comprehensive model contains plasma and 14 tissue compartments, among which TMDD binding process was incorporated in 9 of them, namely the plasma, kidney, liver, spleen, lung, skin, salivary gland, thymus, and reproductive organs. Our final model adequately captured the concentration-time profiles of linagliptin in both plasma and various tissues in both wildtype rats and DPP4-deficient rats following different doses. The association rate constant (k_on) in plasma and tissues were estimated to be 0.943 and 0.00680 nM^-1 h^-1, respectively, and dissociation rate constant (k_off), in plasma and tissues were estimated to be 0.0698 and 0.00880 h^-1, respectively. The binding affinity of linagliptin to DPP-4 (Kd) was predicted to be higher in plasma (0.0740 nM) than that in tissue (1.29 nM). When scaled up to a human, this model captured the substantial and complex nonlinear pharmacokinetic behavior of linagliptin in human adults that is characterized by less-than dose-proportional increase in plasma exposure, dose-dependent clearance and volume of distribution, as well as long terminal half-life with minimal accumulation after repeated doses. Our modeling work is not only novel but also of high significance as the whole-body PBPK-TMDD model platform developed using linagliptin as the model compound could be applied to other small-molecule compounds exhibiting TMDD to facilitate their optimal dose selection. Graphical abstract.

Discovery of BIIB068: A Selective, Potent, Reversible Bruton's Tyrosine Kinase Inhibitor as an Orally Efficacious Agent for Autoimmune Diseases

Autoreactive B cell-derived antibodies form immune complexes that likely play a pathogenic role in autoimmune diseases. In systemic lupus erythematosus (SLE), these antibodies bind Fc receptors on myeloid cells and induce proinflammatory cytokine production by monocytes and NETosis by neutrophils. Bruton's tyrosine kinase (BTK) is a non-receptor tyrosine kinase that signals downstream of Fc receptors and plays a transduction role in antibody expression following B cell activation. Given the roles of BTK in both the production and sensing of autoreactive antibodies, inhibitors of BTK kinase activity may provide therapeutic value to patients suffering from autoantibody-driven immune disorders. Starting from an in-house proprietary screening hit followed by structure-based rational design, we have identified a potent, reversible BTK inhibitor, BIIB068 (1), which demonstrated good kinome selectivity with good overall drug-like properties for oral dosing, was well tolerated across preclinical species at pharmacologically relevant doses with good ADME properties, and achieved >90% inhibition of BTK phosphorylation (pBTK) in humans.

Toxicokinetics and toxicodynamics of the fentanyl homologs cyclopropanoyl-1-benzyl-4´-fluoro-4-anilinopiperidine and furanoyl-1-benzyl-4-anilinopiperidine

The two fentanyl homologs cyclopropanoyl-1-benzyl-4´-fluoro-4-anilinopiperidine (4F-Cy-BAP) and furanoyl-1-benzyl-4-anilinopiperidine (Fu-BAP) have recently been seized as new psychoactive substances (NPS) on the drugs of abuse market. As their toxicokinetic and toxicodynamic characteristics are completely unknown, this study focused on elucidating their in vitro metabolic stability in pooled human liver S9 fraction (pHLS9), their qualitative in vitro (pHLS9), and in vivo (zebrafish larvae) metabolism, and their in vitro isozyme mapping using recombinant expressed isoenzymes. Their maximum-tolerated concentration (MTC) in zebrafish larvae was studied from 0.01 to 100 µM. Their µ-opioid receptor (MOR) activity was analyzed in engineered human embryonic kidney (HEK) 293 T cells. In total, seven phase I and one phase II metabolites of 4F-Cy-BAP and 15 phase I and four phase II metabolites of Fu-BAP were tentatively identified by means of liquid chromatography high-resolution tandem mass spectrometry, with the majority detected in zebrafish larvae. N-Dealkylation, N-deacylation, hydroxylation, and N-oxidation were the most abundant metabolic reactions and the corresponding metabolites are expected to be promising analytical targets for toxicological analysis. Isozyme mapping revealed the main involvement of CYP3A4 in the phase I metabolism of 4F-Cy-BAP and in terms of Fu-BAP additionally CYP2D6. Therefore, drug-drug interactions by CYP3A4 inhibition may cause elevated drug levels and unwanted adverse effects. MTC experiments revealed malformations and changes in the behavior of larvae after exposure to 100 µM Fu-BAP. Both substances were only able to produce a weak activation of MOR and although toxic effects based on MOR activation seem unlikely, activity at other receptors cannot be excluded.

Prediction of paclitaxel pharmacokinetic based on in vitro studies: Interaction with membrane models and human serum albumin

Interactions of paclitaxel (PTX) with models mimicking biological interfaces (lipid membranes and serum albumin, HSA) were investigated to test the hypothesis that the set of in vitro assays proposed can be used to predict some aspects of drug pharmacokinetics (PK). PTX membrane partitioning was studied by derivative spectrophotometry; PTX effect on membrane biophysics was evaluated by dynamic light scattering, fluorescence anisotropy, atomic force microscopy and synchrotron small/wide-angle X-ray scattering; PTX distribution/molecular orientation in membranes was assessed by steady-state/time-resolved fluorescence and computer simulations. PTX binding to HSA was studied by fluorescence quenching, derivative spectrophotometry and dynamic/electrophoretic light scattering. PTX high membrane partitioning is consistent with its efficacy crossing cellular membranes and its off-target distribution. PTX is closely located in the membrane phospholipids headgroups, also interacting with the hydrophobic chains, and causes a major distortion of the alignment of the membrane phospholipids, which, together with its fluidizing effect, justifies some of its cellular toxic effects. PTX binds strongly to HSA, which is consistent with its reduced distribution in target tissues and toxicity by bioaccumulation. In conclusion, the described set of biomimetic models and techniques has the potential for early prediction of PK issues, alerting for the required drug optimizations, potentially minimizing the number of animal tests used in the drug development process.

Further Considerations Towards an Effective and Efficient Oncology Drug Discovery DMPK Strategy

BACKGROUND

DMPK data and knowledge are critical in maximising the probability of developing successful drugs via the application of in silico, in vitro and in vivo approaches in drug discovery.

METHODS

The evaluation, optimisation and prediction of human pharmacokinetics is now a mainstay within drug discovery. These elements are at the heart of the 'right tissue' component of AstraZeneca's '5Rs framework' which, since its adoption, has resulted in increased success of Phase III clinical trials. With the plethora of DMPK related assays and models available, there is a need to continually refine and improve the effectiveness and efficiency of approaches best to facilitate the progression of quality compounds for human clinical testing.

RESULTS

This article builds on previously published strategies from our laboratories, highlighting recent discoveries and successes, that brings our AstraZeneca Oncology DMPK strategy up to date. We review the core aspects of DMPK in Oncology drug discovery and highlight data recently generated in our laboratories that have influenced our screening cascade and experimental design. We present data and our experiences of employing cassette animal PK, as well as re-evaluating in vitro assay design for metabolic stability assessments and expanding our use of freshly excised animal and human tissue to best inform first time in human dosing and dose escalation studies.

CONCLUSION

Application of our updated drug-drug interaction and central nervous system drug exposure strategies are exemplified, as is the impact of physiologically based pharmacokinetic and pharmacokinetic-pharmacodynamic modelling for human predictions.