PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 4: prediction of plasma concentration-time profiles in human from in vivo preclinical data by using the Wajima approach

Translational modelling to predict human pharmacokinetics and pharmacodynamics of a Bruton's tyrosine kinase-targeted protein degrader BGB-16673

BACKGROUND AND PURPOSE

Bifunctional small molecule degraders, which link the target protein with E3 ubiquitin ligase, could lead to the efficient degradation of the target protein. BGB-16673 is a Bruton's tyrosine kinase (BTK) degrader. A translational PK/PD modelling approach was used to predict the human BTK degradation of BGB-16673 from preclinical in vitro and in vivo data.

EXPERIMENTAL APPROACH

A simplified mechanistic PK/PD model was used to establish the correlation between the in vitro and in vivo BTK degradation by BGB-16673 in a mouse model. Human and mouse species differences were compared using the parameters generated from in vitro human or mouse blood, and human or mouse serum spiked TMD-8 cells. Human PD was then predicted using the simplified mechanistic PK/PD model.

KEY RESULTS

BGB-16673 showed potent BTK degradation in mouse whole blood, human whole blood, and TMD-8 tumour cells in vitro. Furthermore, BGB-16673 showed BTK degradation in a murine TMD-8 xenograft model in vivo. The PK/PD model predicted human PD and the observed BTK degradation in clinical studies both showed robust BTK degradation in blood and tumour at clinical dose range.

CONCLUSION AND IMPLICATIONS

The presented simplified mechanistic model with reduced number of model parameters is practically easier to be applied to research projects compared with the full mechanistic model. It can be used as a tool to better understand the PK/PD behaviour for targeted protein degraders and increase the confidence when moving to the clinical stage.

EANM guidance document: dosimetry for first-in-human studies and early phase clinical trials

The numbers of diagnostic and therapeutic nuclear medicine agents under investigation are rapidly increasing. Both novel emitters and novel carrier molecules require careful selection of measurement procedures. This document provides guidance relevant to dosimetry for first-in human and early phase clinical trials of such novel agents. The guideline includes a short introduction to different emitters and carrier molecules, followed by recommendations on the methods for activity measurement, pharmacokinetic analyses, as well as absorbed dose calculations and uncertainty analyses. The optimal use of preclinical information and studies involving diagnostic analogues is discussed. Good practice reporting is emphasised, and relevant dosimetry parameters and method descriptions to be included are listed. Three examples of first-in-human dosimetry studies, both for diagnostic tracers and radionuclide therapies, are given.

Building a Predictive PBPK Model for Human OATP Substrates: a Strategic Framework for Early Evaluation of Clinical Pharmacokinetic Variations Using Pitavastatin as an Example

To select a drug candidate for clinical development, accurately and promptly predicting human pharmacokinetic (PK) profiles, assessing drug-drug interactions (DDIs), and anticipating potential PK variations in disease populations are crucial steps in drug discovery. The complexity of predicting human PK significantly increases when hepatic transporters are involved in drug clearance (CL) and volume of distribution (Vss). A strategic framework is developed here, utilizing pitavastatin as an example. The framework includes the construction of a monkey physiologically-based PK (PBPK) model, model calibration to obtain scaling factors (SF) of in vitro-in vivo extrapolation (IVIVE) for various clearance parameters, human model development and validation, and assessment of DDIs and PK variations in disease populations. Through incorporating in vitro human parameters and calibrated SFs from the monkey model of 3.45, 0.14, and 1.17 for CLint,active, CLint,passive, and CLint,bile, respectively, and together with the relative fraction transported by individual transporters obtained from in vitro studies and the optimized Ki values for OATP inhibition, the model reasonably captured observed pitavastatin PK profiles, DDIs and PK variations in human subjects carrying genetic polymorphisms, i.e., AUC within 20%. Lastly, when applying the functional reduction based on measured OATP1B biomarkers, the model adequately predicted PK changes in the hepatic impairment population. The present study presents a strategic framework for early-stage drug development, enabling the prediction of PK profiles and assessment of PK variations in scenarios like DDIs, genetic polymorphism, and hepatic impairment-related disease states, specifically focusing on OATP substrates.

Empirical scaling factor for predicting human pharmacokinetic profiles of disproportionate metabolites using the Css-MRTpo method and chimeric mice with humanised livers

Predicting plasma concentration-time profiles of disproportionate metabolites in humans is crucial for evaluating metabolites according to the Safety Testing guidelines. We evaluated Css-MRTpo, an empirical method, using chimeric mice with humanised livers capable of generating human-disproportionate metabolites. Azilsartan and AZ-M2 were administered to humanised chimeric mice, and pharmacokinetic parameters were obtained. Pharmacokinetic data for DS-1971a and DS-M1 in humanised chimeric mice were obtained from the literature. The human plasma concentration-time profiles of these compounds were simulated using the Css-MRTpo method. Azilsartan, DS-1971a, and PF-04937319 produced human disproportionate metabolites, AZ-M2, DS-M1, and PF-M1, respectively. The predicted human pharmacokinetic profiles of PF-04937319 and PF-M1 were obtained from a previous study, and their outcomes were re-evaluated. Our findings revealed that the plasma concentrations of the three metabolites were unexpectedly underpredicted, whereas the three unchanged drugs were reasonably predicted. Further, the introduction of the empirical scaling factor of 3, obtained from six model compounds, improved the predictability of metabolites, suggesting the potential usefulness of the Css-MRTpo method in combination with humanised chimeric mice for predicting the pharmacokinetic profiles of disproportionate metabolites at the early stage of new drug development.

Quantitative Prediction of OATP-Mediated Disposition and Biliary Clearance Using Human Liver Chimeric Mice

Drug biliary clearance (CLbile) in vivo is among the most difficult pharmacokinetic parameters to predict accurately and quantitatively because biliary excretion is influenced by metabolic enzymes, transporters, and passive diffusion across hepatocyte membranes. The purpose of this study is to demonstrate the use of Hu-FRG mice [Fah-/-/Rag2-/-/Il2rg-/- (FRG) mice transplanted with human-derived hepatocytes] to quantitatively predict human organic anion transporting polypeptide (OATP)-mediated drug disposition and CLbile To predict OATP-mediated disposition, six OATP substrates (atorvastatin, fexofenadine, glibenclamide, pitavastatin, pravastatin, and rosuvastatin) were administered intravenously to Hu-FRG and Mu-FRG mice (FRG mice transplanted with mouse hepatocytes) with or without rifampicin as an OATP inhibitor. We calculated the hepatic intrinsic clearance (CLh,int) and the change of hepatic clearance (CLh) caused by rifampicin (CLh ratio). We compared the CLh,int of humans with that of Hu-FRG mice and the CLh ratio of humans with that of Hu-FRG and Mu-FRG mice. For predicting CLbile, 20 compounds (two cassette doses of 10 compounds) were administered intravenously to gallbladder-cannulated Hu-FRG and Mu-FRG mice. We evaluated the CLbile and investigated the correlation of human CLbile with that of Hu-FRG and Mu-FRG mice. We found good correlations between humans and Hu-FRG mice in CLh,int (100% within threefold) and CLh ratio (R2 = 0.94). Moreover, we observed a much better relationship between humans and Hu-FRG mice in CLbile (75% within threefold). Our results suggest that OATP-mediated disposition and CLbile can be predicted using Hu-FRG mice, making them a useful in vivo drug discovery tool for quantitatively predicting human liver disposition. SIGNIFICANCE STATEMENT: OATP-mediated disposition and biliary clearance of drugs are likely quantitatively predictable using Hu-FRG mice. The findings can enable the selection of better drug candidates and the development of more effective strategies for managing OATP-mediated DDIs in clinical studies.

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 naïve pooled data approach for extrapolation of Phase 0 microdose trials to therapeutic dosing regimens

Microdosing is a strategy to obtain knowledge of human pharmacokinetics prior to Phase I clinical trials. The most frequently used method to extrapolate microdose (≤100 μg) pharmacokinetics to therapeutic doses is based on linear extrapolation from a noncompartmental analysis (NCA) with a two-fold acceptance criterion between pharmacokinetic metrics of the extrapolated microdose and the therapeutic dose. The major disadvantage of NCA is the assumption of linear extrapolation of NCA metrics. In this study, we used a naïve pooled data (NPD) modeling approach to extrapolate microdose pharmacokinetics to therapeutic pharmacokinetics. Gemcitabine and anastrozole were used as examples of intravenous and oral drugs, respectively. Data from microdose studies were used to build a parent-metabolite model for gemcitabine and its metabolite 2',2'-difluorodeoxyuridine (dFdU) and a model for anastrozole. The pharmacokinetic microdose models were extrapolated to therapeutic doses. Extrapolation of the microdose showed differences in pharmacokinetic shape for gemcitabine and dFdU between the simulated and observed therapeutic concentrations, whereas the observed therapeutic concentrations for anastrozole were captured by the extrapolation. This study demonstrated the possible use and feasibility of an NPD modeling approach for the evaluation and application of microdose studies in early drug development. Last, physiologically-based pharmacokinetic modeling might be an alternative for microdose extrapolation of drugs with complex pharmacokinetics such as gemcitabine.

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.

Evaluation of the Success of High-Throughput Physiologically Based Pharmacokinetic (HT-PBPK) Modeling Predictions to Inform Early Drug Discovery

Minimizing in vitro and in vivo testing in early drug discovery with the use of physiologically based pharmacokinetic (PBPK) modeling and machine learning (ML) approaches has the potential to reduce discovery cycle times and animal experimentation. However, the prediction success of such an approach has not been shown for a larger and diverse set of compounds representative of a lead optimization pipeline. In this study, the prediction success of the oral (PO) and intravenous (IV) pharmacokinetics (PK) parameters in rats was assessed using a “bottom-up” approach, combining in vitro and ML inputs with a PBPK model. More than 240 compounds for which all of the necessary inputs and PK data were available were used for this assessment. Different clearance scaling approaches were assessed, using hepatocyte intrinsic clearance and protein binding as inputs. In addition, a novel high-throughput PBPK (HT-PBPK) approach was evaluated to assess the scalability of PBPK predictions for a larger number of compounds in drug discovery. The results showed that bottom-up PBPK modeling was able to predict the rat IV and PO PK parameters for the majority of compounds within a 2- to 3-fold error range, using both direct scaling and dilution methods for clearance predictions. The use of only ML-predicted inputs from the structure did not perform well when using in vitro inputs, likely due to clearance miss predictions. The HT-PBPK approach produced comparable results to the full PBPK modeling approach but reduced the simulation time from hours to seconds. In conclusion, a bottom-up PBPK and HT-PBPK approach can successfully predict the PK parameters and guide early discovery by informing compound prioritization, provided that good in vitro assays are in place for key parameters such as clearance.

Pharmacokinetics of 40 kDa Polyethylene glycol (PEG) in mice, rats, cynomolgus monkeys and predicted pharmacokinetics in humans

Conjugation with polyethylene glycol (PEG), PEGylation, has been considered a useful tool to improve drug-like properties of novel small molecules and biologics in drug discovery. PEG40 or 40 kDa PEG is a double-branched PEG, routinely employed to improve the pharmacokinetics (PK) of therapeutics, including successful marketed products such as Pegasys® and Omontys®. However, less is known about the extent of contribution of PEG40 to the overall PK of the PEGylated product. Considering the half-life of PEG40 conjugated PEGylated products ranges from 1 to 14 days in human, this information is immensely valuable. After successfully developing a high sensitivity NMR based analytical method to quantitate PEG40 in mice serum after intravenous (IV) administration (Khandelwal et al., 2019), here, we extend its application to measure PEG40 in serum after IV administration and subcutaneous (SC) absorption in routinely employed non-clinical species in drug discovery, namely, mice, rats and cynomolgus monkeys. We utilized non-compartmental analysis and compartmental modeling to characterize the PK of PEG40 in these non-clinical species. Finally, we employed allometric scaling and Wajima (MRT-Css) method to predict the PK of PEG40 in human after IV administration and SC absorption. In general, our data shows that intrinsic PK parameters of PEG40 in mice, rats and cynomolgus monkeys are in the range of published literature values for PEG40-conjugated products, unless saturable clearance mechanisms are involved. We observed a bioavailability (F) of ~68% in CD-1 mice after SC administration of PEG40. In rats, the clearance (CL) and volume of distribution at steady state (V_ss) after IV infusion of PEG40 were 0.079 mL/min/kg and 0.19 L/kg, respectively; and SC bioavailability was ~20%. In cynomolgus monkeys, after IV infusion, CL and V_ss of PEG40 were 0.037 mL/min/kg and 0.20 L/kg, respectively; and SC bioavailability was ~69%. In addition, our findings indicate flip-flop kinetics of PEG40 in rodents, but not in cynomolgus monkeys. Finally, in human, intrinsic CL and V_ss of PEG40 were projected to be 0.02 mL/min/kg (0.084 L/h) and 0.22 L/kg, respectively. This comprehensive report of PK of PEG40 in non-clinical species and its subsequent prediction in humans is expected to be useful to drug discovery and development scientists for efficient decision-making and optimal resource utilization.

Predictive Value of Microdose Pharmacokinetics

Phase 0 microdose trials are exploratory studies to early assess human pharmacokinetics of new chemical entities, while limiting drug exposure and risks for participants. The microdose concept is based on the assumption that microdose pharmacokinetics can be extrapolated to pharmacokinetics of a therapeutic dose. However, it is unknown whether microdose pharmacokinetics are actually indicative of the pharmacokinetics at therapeutic dose. The aim of this review is to investigate the predictive value of microdose pharmacokinetics and to identify drug characteristics that may influence the scalability of these parameters. The predictive value of microdose pharmacokinetics was determined for 46 compounds and showed adequate predictability for 28 of 41 orally administered drugs (68%) and 15 of 16 intravenously administered drugs (94%). Microdose pharmacokinetics were considered predictive if the mean observed values of the microdose and the therapeutic dose were within twofold. Nonlinearity may be caused by saturation of enzyme and transporter systems, such as intestinal and hepatic efflux and uptake transporters. The high degree of success regarding linear pharmacokinetics shows that phase 0 microdose trials can be used as an early human model for determination of drug pharmacokinetics.

Improving the Accuracy of Predicted Human Pharmacokinetics: Lessons Learned from the AstraZeneca Drug Pipeline Over Two Decades

During drug discovery and prior to the first human dose of a novel candidate drug, the pharmacokinetic (PK) behavior of the drug in humans is predicted from preclinical data. This helps to inform the likelihood of achieving therapeutic exposures in early clinical development. Once clinical data are available, the observed human PK are compared with predictions, providing an opportunity to assess and refine prediction methods. Application of best practice in experimental data generation and predictive methodologies, and a focus on robust mechanistic understanding of the candidate drug disposition properties before nomination to clinical development, have led to maximizing the probability of successful PK predictions so that 83% of AstraZeneca drug development projects progress in the clinic with no PK issues; and 71% of key PK parameter predictions [64% of area under the curve (AUC) predictions; 78% of maximum concentration (C_max) predictions; and 70% of half-life predictions] are accurate to within twofold. Here, we discuss methods to predict human PK used by AstraZeneca, how these predictions are assessed and what can be learned from evaluating the predictions for 116 candidate drugs.

A novel Css-MRTpo approach to simulate oral plasma concentration-time profiles of the partial glucokinase activator PF-04937319 and its disproportionate N-demethylated metabolite in humans using chimeric mice with humanized livers

A Css-MRTpo superposition method was devised to predict (retrospectively) oral plasma concentration-time profiles of PF-04937319 and its MIST-related metabolite, M1, in humans using chimeric mice with humanized liver.Original PK data were taken from a published report in which PF-04937319 and M1 were given to chimeric mice orally and/or intravenously. Human CL and Vss were predicted by single-species allometry and MRTiv,pred were calculated as Vss,pred/CL,pred. MRTpo,human were assumed to be MRTiv,pred plus MAT or mean metabolite formation time (MFT). Human Css was calculated by dividing the corrected oral dose by Vss,pred.Chronological sampling time and measured plasma concentrations were corrected by MRTpo,human and Css,human, respectively, and transformed to the corresponding values in humans.The obtained concentration-time profile of PF-04937319 was superimposed well with the observed data after single and repeated oral administration to humans. The transformed plasma concentration of M1 was somewhat lower than the observed value, but a slow increase of the simulated metabolite reflected gradual increase of observed M1 on Day 1. Transformed M1 gave an almost-flat concentration-time profile on Day 14, which was consistent with the curve observed in humans. Application of this novel method to other MIST-related compounds is discussed.

Current trends in drug metabolism and pharmacokinetics

Pharmacokinetics (PK) is the study of the absorption, distribution, metabolism, and excretion (ADME) processes of a drug. Understanding PK properties is essential for drug development and precision medication. In this review we provided an overview of recent research on PK with focus on the following aspects: (1) an update on drug-metabolizing enzymes and transporters in the determination of PK, as well as advances in xenobiotic receptors and noncoding RNAs (ncRNAs) in the modulation of PK, providing new understanding of the transcriptional and posttranscriptional regulatory mechanisms that result in inter-individual variations in pharmacotherapy; (2) current status and trends in assessing drug-drug interactions, especially interactions between drugs and herbs, between drugs and therapeutic biologics, and microbiota-mediated interactions; (3) advances in understanding the effects of diseases on PK, particularly changes in metabolizing enzymes and transporters with disease progression; (4) trends in mathematical modeling including physiologically-based PK modeling and novel animal models such as CRISPR/Cas9-based animal models for DMPK studies; (5) emerging non-classical xenobiotic metabolic pathways and the involvement of novel metabolic enzymes, especially non-P450s. Existing challenges and perspectives on future directions are discussed, and may stimulate the development of new research models, technologies, and strategies towards the development of better drugs and improved clinical practice.

Preclinical assessment of the ADME, efficacy and drug-drug interaction potential of a novel NAMPT inhibitor

GNE-617 (N-(4-((3,5-difluorophenyl)sulfonyl)benzyl)imidazo[1,2-a]pyridine-6-carboxamide) is a potent, selective nicotinamide phosphoribosyltransferase (NAMPT) inhibitor being explored as a potential treatment for human cancers. Plasma clearance was low in monkeys and dogs (9.14 mL min^-1kg^-1 and 4.62 mL min^-1kg^-1, respectively) and moderate in mice and rats (36.4 mL min^-1kg^-1 and 19.3 mL min^-1kg^-1, respectively). Oral bioavailability in mice, rats, monkeys and dogs was 29.7, 33.9, 29.4 and 65.2%, respectively. Allometric scaling predicted a low clearance of 3.3 mL min^-1kg^-1 and a volume of distribution of 1.3 L kg^-1 in human. Efficacy (57% tumor growth inhibition) in Colo-205 CRC tumor xenograft mice was observed at an oral dose of 15 mg/kg BID (AUC = 10.4 µM h). Plasma protein binding was moderately high. GNE-617 was stable to moderately stable in vitro. Main human metabolites identified in human hepatocytes were formed primarily by CYP3A4/5. Transporter studies suggested that GNE-617 is likely a substrate for MDR1 but not for BCRP. Simcyp^® simulations suggested a low (CYP2C9 and CYP2C8) or moderate (CYP3A4/5) potential for drug-drug interactions. The potential for autoinhibition was low. Overall, GNE-617 exhibited acceptable preclinical properties and projected human PK and dose estimates.

Pharmacokinetics and interspecies scaling of a novel, orally-bioavailable anti-cancer drug, SHetA2

SHetA2 is a small molecule drug with promising cancer prevention and therapeutic activity and a high preclinical safety profile. The study objectives were to perform interspecies scaling and pharmacokinetic (PK) modeling of SHetA2 for human PK prediction. The PK data obtained from mice, rats, and dogs after intravenous and oral doses were used for simultaneous fitting to PK models. The disposition of SHetA2 was best described by a two-compartment model. The absorption kinetics was well characterized with a first-order absorption model for mice and rats, and a gastrointestinal transit model for dogs. Oral administration of SHetA2 showed a relatively fast absorption in mice, prolonged absorption (i.e., flip-flop kinetics) toward high doses in rats, and an early peak followed by a secondary peak at high doses in dogs. The oral bioavailability was 17.7-19.5% at 20-60 mg/kg doses in mice, <1.6% at 100-2000 mg/kg in rats, and 11.2% at 100 mg/kg decreasing to 3.45% at 400 mg/kg and 1.11% at 1500 mg/kg in dogs. The disposition parameters were well correlated with the body weight for all species using the allometric equation, which predicted values of CL (17.3 L/h), V1 (36.2 L), V2 (68.5 L) and CLD (15.2 L/h) for a 70-kg human. The oral absorption rate and bioavailability of SHetA2 was highly dependent on species, doses, formulations, and possibly other factors. The limited bioavailability at high doses was taken into consideration for the suggested first-in-human dose, which was much lower than the dose estimated based on toxicology studies. In summary, the present study provided the PK model for SHetA2 that depicted the disposition and absorption kinetics in preclinical species, and computational tools for human PK prediction.

A human microdose study of the antimalarial drug GSK3191607 in healthy volunteers

AIMS

GSK3191607, a novel inhibitor of the Plasmodium falciparum ATP4 (PfATP4) pathway, is being considered for development in humans. However, a key problem encountered during the preclinical evaluation of the compound was its inconsistent pharmacokinetic (PK) profile across preclinical species (mouse, rat and dog), which prevented reliable prediction of PK parameters in humans and precluded a well-founded assessment of the potential for clinical development of the compound. Therefore, an open-label microdose (100 μg, six subjects) first time in humans study was conducted to assess the human PK of GSK3191607 following intravenous administration of [14C]-GSK3191607.

METHODS

A human microdose study was conducted to investigate the clinical PK of GSK3191607 and enable a Go/No Go decision on further progression of the compound. The PK disposition parameters estimated from the microdose study, combined with preclinical in vitro and in vivo pharmacodynamic parameters, were all used to estimate the potential efficacy of various oral dosing regimens in humans.

RESULTS

The PK profile, based on the microdose data, demonstrated a half-life (~17 h) similar to other antimalarial compounds currently in clinical development. However, combining the microdose data with the pharmacodynamic data provided results that do not support further clinical development of the compound for a single dose cure.

CONCLUSIONS

The information generated by this study provides a basis for predicting the expected oral PK profiles of GSK3191607 in man and supports decisions on the future clinical development of the compound.

Midostaurin, a Novel Protein Kinase Inhibitor for the Treatment of Acute Myelogenous Leukemia: Insights from Human Absorption, Metabolism, and Excretion Studies of a BDDCS II Drug

The absorption, metabolism, and excretion of midostaurin, a potent class III tyrosine protein kinase inhibitor for acute myelogenous leukemia, were evaluated in healthy subjects. A microemulsion formulation was chosen to optimize absorption. After a 50-mg [¹⁴C]midostaurin dose, oral absorption was high (>90%) and relatively rapid. In plasma, the major circulating components were midostaurin (22%), CGP52421 (32.7%), and CGP62221 (27.7%). Long plasma half-lives were observed for midostaurin (20.3 hours), CGP52421 (495 hours), and CGP62221 (33.4 hours). Through careful mass-balance study design, the recovery achieved was good (81.6%), despite the long radioactivity half-lives. Most of the radioactive dose was recovered in feces (77.6%) mainly as metabolites, because only 3.43% was unchanged, suggesting mainly hepatic metabolism. Renal elimination was minor (4%). Midostaurin metabolism pathways involved hydroxylation, O-demethylation, amide hydrolysis, and N-demethylation. High plasma CGP52421 and CGP62221 exposures in humans, along with relatively potent cell-based IC₅₀ for FMS-like tyrosine kinase 3-internal tandem duplications inhibition, suggested that the antileukemic activity in AML patients may also be maintained by the metabolites. Very high plasma protein binding (>99%) required equilibrium gel filtration to identify differences between humans and animals. Because midostaurin, CGP52421, and CGP62221 are metabolized mainly by CYP3A4 and are inhibitors/inducers for CYP3A, potential drug-drug interactions with mainly CYP3A4 modulators/CYP3A substrates could be expected. Given its low aqueous solubility, high oral absorption and extensive metabolism (>90%), midostaurin is a Biopharmaceutics Classification System/Biopharmaceutics Drug Disposition Classification System (BDDCS) class II drug in human, consistent with rat BDDCS in vivo data showing high absorption (>90%) and extensive metabolism (>90%).

Prediction of Deoxypodophyllotoxin Disposition in Mouse, Rat, Monkey, and Dog by Physiologically Based Pharmacokinetic Model and the Extrapolation to Human

Deoxypodophyllotoxin (DPT) is a potential anti-tumor candidate prior to its clinical phase. The aim of the study was to develop a physiologically based pharmacokinetic (PBPK) model consisting of 13 tissue compartments to predict DPT disposition in mouse, rat, monkey, and dog based on in vitro and in silico inputs. Since large interspecies difference was found in unbound fraction of DPT in plasma, we assumed that K_t:pl,u (unbound tissue-to-plasma concentration ratio) was identical across species. The predictions of our model were then validated by in vivo data of corresponding preclinical species, along with visual predictive checks. Reasonable matches were found between observed and predicted plasma concentrations and pharmacokinetic parameters in all four animal species. The prediction in the related seven tissues of mouse was also desirable. We also attempted to predict human pharmacokinetic profile by both the developed PBPK model and interspecies allometric scaling across mouse, rat and monkey, while dog was excluded from the scaling. The two approaches reached similar results. We hope the study will help in the efficacy and safety assessment of DPT in future clinical studies and provide a reference to the preclinical screening of similar compounds by PBPK model.

Evaluation of the GastroPlus™ Advanced Compartmental and Transit (ACAT) Model in Early Discovery

PURPOSE

The aim of this study was to evaluate the oral exposure predictions obtained early in drug discovery with a generic GastroPlus Advanced Compartmental And Transit (ACAT) model based on the in vivo intravenous blood concentration-time profile, in silico properties (lipophilicity, pKa) and in vitro high-throughput absorption-distribution-metabolism-excretion (ADME) data (as determined by PAMPA, solubility, liver microsomal stability assays).

METHODS

The model was applied to a total of 623 discovery molecules and their oral exposure was predicted in rats and/or dogs. The predictions of Cmax, AUClast and Tmax were compared against the observations.

RESULTS

The generic model proved to make predictions of oral Cmax, AUClast and Tmax within 3-fold of the observations for rats in respectively 65%, 68% and 57% of the 537 cases. For dogs, it was respectively 77%, 79% and 85% of the 124 cases. Statistically, the model was most successful at predicting oral exposure of Biopharmaceutical Classification System (BCS) class 1 compounds compared to classes 2 and 3, and was worst at predicting class 4 compounds oral exposure.

CONCLUSION

The generic GastroPlus ACAT model provided reasonable predictions especially for BCS class 1 compounds. For compounds of other classes, the model may be refined by obtaining more information on solubility and permeability in secondary assays. This increases confidence that such a model can be used in discovery projects to understand the parameters limiting absorption and extrapolate predictions across species. Also, when predictions disagree with the observations, the model can be updated to test hypotheses and understand oral absorption.

Quantitative prediction of human pharmacokinetics and pharmacodynamics of imigliptin, a novel DPP-4 inhibitor, using allometric scaling, IVIVE and PK/PD modeling methods

PURPOSE

To predict the pharmacokinetic/pharmacodynamic (PK/PD) profiles of imigliptin, a novel DPP-4 inhibitor, in first-in-human (FIH) study based on the data from preclinical species.

METHODS

Imigliptin was intravenously and orally administered to rats, dogs, and monkeys to assess their PK/PD properties. DPP-4 activity was the PD biomarker. PK/PD profiles of sitagliptin and alogliptin in rats and humans were obtained and digitized from literatures. PK/PD profiles of all dose levels for each drug in each species were analyzed using modeling approach. Human CL, Vss and PK profiles of imigliptin were then predicted using Allometric Scaling (AS), in vitro in vivo extrapolation (IVIVE), and the steady-state plasma drug concentration - mean residence time (Css-MRT) methods. In vitro EC50 corrected by fu and in vivo EC50 in rats corrected by interspecies difference of sitagliptin and alogliptin were utilized separately to predict imigliptin human EC50. The prediction by integrating all above methods was evaluated by comparing observed and simulated PK/PD profiles in healthy subjects.

RESULTS

Full PK/PD profiles in animal were summarized for imigliptin, sitagliptin and alogliptin. Imigliptin CL, Vss, and Fa were predicted to be 19.1L/h, 247L, and 0.81 in humans, respectively. Predicted imigliptin AUCs, AUECs, and Emax in humans were within 0.8-1.2 times of observed values whereas other predicted PK/PD parameters were within 0.5-1.5 times of observed values.

CONCLUSIONS

By integrating available preclinical and clinical data, FIH PK/PD profiles of imigliptin could be accurately predicted.

Systematic Evaluation of Wajima Superposition (Steady-State Concentration to Mean Residence Time) in the Estimation of Human Intravenous Pharmacokinetic Profile

We present a systematic evaluation of the Wajima superpositioning method to estimate the human intravenous (i.v.) pharmacokinetic (PK) profile based on a set of 54 marketed drugs with diverse structure and range of physicochemical properties. We illustrate the use of average of "best methods" for the prediction of clearance (CL) and volume of distribution at steady state (VDss) as described in our earlier work (Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):178-191; Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):167-177). These methods provided much more accurate prediction of human PK parameters, yielding 88% and 70% of the prediction within 2-fold error for VDss and CL, respectively. The prediction of human i.v. profile using Wajima superpositioning of rat, dog, and monkey time-concentration profiles was tested against the observed human i.v. PK using fold error statistics. The results showed that 63% of the compounds yielded a geometric mean of fold error below 2-fold, and an additional 19% yielded a geometric mean of fold error between 2- and 3-fold, leaving only 18% of the compounds with a relatively poor prediction. Our results showed that good superposition was observed in any case, demonstrating the predictive value of the Wajima approach, and that the cause of poor prediction of human i.v. profile was mainly due to the poorly predicted CL value, while VDss prediction had a minor impact on the accuracy of human i.v. profile prediction.

Optimization of human dose prediction by using quantitative and translational pharmacology in drug discovery

In this perspective article, we explain how quantitative and translational pharmacology, when well-implemented, is believed to lead to improved clinical candidates and drug targets that are differentiated from current treatment options. Quantitative and translational pharmacology aims to build and continuously improve the quantitative relationship between drug exposure, target engagement, efficacy, safety and its interspecies relationship at every phase of drug discovery. Drug hunters should consider and apply these concepts to develop compounds with a higher probability of interrogating the clinical biological hypothesis. We offer different approaches to set an initial effective concentration or pharmacokinetic-pharmacodynamic target in man and to predict human pharmacokinetics that determine together the predicted human dose and dose schedule. All concepts are illustrated with ample literature examples.

Human absorption, distribution, metabolism and excretion properties of drug molecules: a plethora of approaches

Human radiolabel studies are traditionally conducted to provide a definitive understanding of the human absorption, distribution, metabolism and excretion (ADME) properties of a drug. However, advances in technology over the past decade have allowed alternative methods to be employed to obtain both clinical ADME and pharmacokinetic (PK) information. These include microdose and microtracer approaches using accelerator mass spectrometry, and the identification and quantification of metabolites in samples from classical human PK studies using technologies suitable for non-radiolabelled drug molecules, namely liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. These recently developed approaches are described here together with relevant examples primarily from experiences gained in support of drug development projects at GlaxoSmithKline. The advantages of these study designs together with their limitations are described. We also discuss special considerations which should be made for a successful outcome to these new approaches and also to the more traditional human radiolabel study in order to maximize knowledge around the human ADME properties of drug molecules.

In vitro platforms for evaluating liver toxicity

The liver is a heterogeneous organ with many vital functions, including metabolism of pharmaceutical drugs and is highly susceptible to injury from these substances. The etiology of drug-induced liver disease is still debated although generally regarded as a continuum between an activated immune response and hepatocyte metabolic dysfunction, most often resulting from an intermediate reactive metabolite. This debate stems from the fact that current animal and in vitro models provide limited physiologically relevant information, and their shortcomings have resulted in "silent" hepatotoxic drugs being introduced into clinical trials, garnering huge financial losses for drug companies through withdrawals and late stage clinical failures. As we advance our understanding into the molecular processes leading to liver injury, it is increasingly clear that (a) the pathologic lesion is not only due to liver parenchyma but is also due to the interactions between the hepatocytes and the resident liver immune cells, stellate cells, and endothelial cells; and (b) animal models do not reflect the human cell interactions. Therefore, a predictive human, in vitro model must address the interactions between the major human liver cell types and measure key determinants of injury such as the dosage and metabolism of the drug, the stress response, cholestatic effect, and the immune and fibrotic response. In this mini-review, we first discuss the current state of macro-scale in vitro liver culture systems with examples that have been commercialized. We then introduce the paradigm of microfluidic culture systems that aim to mimic the liver with physiologically relevant dimensions, cellular structure, perfusion, and mass transport by taking advantage of micro and nanofabrication technologies. We review the most prominent liver-on-a-chip platforms in terms of their physiological relevance and drug response. We conclude with a commentary on other critical advances such as the deployment of fluorescence-based biosensors to identify relevant toxicity pathways, as well as computational models to create a predictive tool.

Contribution of cytochrome P450 and UDT-glucuronosyltransferase to the metabolism of drugs containing carboxylic acid groups: risk assessment of acylglucuronides using human hepatocytes

1. In order to evaluate the inhibition activity of 1-aminobenzotriazole (ABT) and (-)-borneol (borneol) against cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), the substrates of these metabolic enzymes were incubated with ABT and borneol in human hepatocytes. We found that 3 mM ABT and 300 μM borneol were the most suitable experimental levels to specifically inhibit CYP and UGT. 2. Montelukast, mefenamic acid, flufenamic acid, diclofenac, tienilic acid, gemfibrozil, ibufenac and repaglinide were markedly metabolized in human hepatocytes, and the metabolism of gemfibrozil, mefenamic acid and flufenamic acid was inhibited by borneol. With regard to repaglinide, montelukast, diclofenac and tienilic acid, metabolism was inhibited by ABT. Ibufenac was partly inhibited by both inhibitors. Zomepirac, tolmetin, ibuprofen, indomethacin and levofloxacin were moderately metabolized by human hepatocytes, and the metabolism of zomepirac, ibuprofen and indomethacin was equally inhibited by both ABT and borneol. The metabolism of tolmetin was strongly inhibited by ABT, and was also inhibited weakly by borneol. Residual drugs, telmisartan, valsartan, furosemide, naproxen and probenecid were scarcely metabolized. 3. Although we attempted to predict the toxicological risks of drugs containing carboxylic groups from the combination chemical stability and CLint via UGT, the results indicated that this combination was not sufficient and that clinical daily dose is important.

Prediction of drug disposition on the basis of its chemical structure

The chemical structure of any drug determines its pharmacokinetics and pharmacodynamics. Detailed understanding of relationships between the drug chemical structure and individual disposition pathways (i.e., distribution and elimination) is required for efficient use of existing drugs and effective development of new drugs. Different approaches have been developed for this purpose, ranging from statistics-based quantitative structure-property (or structure-pharmacokinetic) relationships (QSPR) analysis to physiologically based pharmacokinetic (PBPK) models. This review critically analyzes currently available approaches for analysis and prediction of drug disposition on the basis of chemical structure. Models that can be used to predict different aspects of disposition are presented, including: (a) value of the individual pharmacokinetic parameter (e.g., clearance or volume of distribution), (b) efficiency of the specific disposition pathway (e.g., biliary drug excretion or cytochrome P450 3A4 metabolism), (c) accumulation in a specific organ or tissue (e.g., permeability of the placenta or accumulation in the brain), and (d) the whole-body disposition in the individual patients. Examples of presented pharmacological agents include "classical" low-molecular-weight compounds, biopharmaceuticals, and drugs encapsulated in specialized drug-delivery systems. The clinical efficiency of agents from all these groups can be suboptimal, because of inefficient permeability of the drug to the site of action and/or excessive accumulation in other organs and tissues. Therefore, robust and reliable approaches for chemical structure-based prediction of drug disposition are required to overcome these limitations. PBPK models are increasingly being used for prediction of drug disposition. These models can reflect the complex interplay of factors that determine drug disposition in a mechanistically correct fashion and can be combined with other approaches, for example QSPR-based prediction of drug permeability and metabolism, pharmacogenomic data and tools, pharmacokinetic-pharmacodynamic modeling approaches, etc. Moreover, the PBPK models enable detailed analysis of clinically relevant scenarios, for example the effect of the specific conditions on the time course of the analyzed drug in the individual organs and tissues, including the site of action. It is expected that further development of such combined approaches will increase their precision, enhance the effectiveness of drugs, and lead to individualized drug therapy for different patient populations (geriatric, pediatric, specific diseases, etc.).

Microdosing and drug development: past, present and future

INTRODUCTION

Microdosing is an approach to early drug development where exploratory pharmacokinetic data are acquired in humans using inherently safe sub-pharmacologic doses of drug. The first publication of microdose data was 10 years ago and this review comprehensively explores the microdose concept from conception, over the past decade, up until the current date.

AREAS COVERED

The authors define and distinguish the concept of microdosing from similar approaches. The authors review the ability of microdosing to provide exploratory pharmacokinetics (concentration-time data) but exclude microdosing using positron emission tomography. The article provides a comprehensive review of data within the peer-reviewed literature as well as the latest applications and a look into the future, towards where microdosing may be headed.

EXPERT OPINION

Evidence so far suggests that microdosing may be a better predictive tool of human pharmacokinetics than alternative methods and combination with physiologically based modelling may lead to much more reliable predictions in the future. The concept has also been applied to drug-drug interactions, polymorphism and assessing drug concentrations over time at its site of action. Microdosing may yet have more to offer in unanticipated directions and provide benefits that have not been fully realised to date.

Nilotinib preclinical pharmacokinetics and practical application toward clinical projections of oral absorption and systemic availability

Nilotinib is a highly potent and selective bcr-abl tyrosine kinase inhibitor used for the treatment of patients who are in the chronic and accelerated phases of Philadelphia chromosome-positive (Ph+) chronic myeloid leukemia (CML). Nilotinib preclinical data and its use for practical predictions of systemic exposure profiles and oral absorption are described. The systemic clearance (CL) of nilotinib was relatively low in rodents with a value of less than 25% of hepatic blood flow (Q(H)), while it was moderate in monkeys and dogs (CL/Q(H)  = 32-35%). The steady state volume of distribution (V(ss) ) ranged from 0.55 to 3.9 l/kg across the species tested. The maximum concentration (C(max)) of nilotinib occurred at 0.5-4 h and the bioavailability was moderate (17-44%). The plasma protein binding was high (> 97.5%) in preclinical species and humans. The human CL (~ 0.1 l/h/kg) and V(ss) (~2.0 l/kg) were best predicted by the rat-dog-human proportionality method and allometric scaling method, respectively. The human intravenous pharmacokinetic profile was projected by the Wajima 'C(ss)-MRT' method. The predicted micro-constants from human intravenous profiles were incorporated into the advanced compartmental absorption and transit model within the GastroPlus program to simulate the oral concentration-time curves in humans. Overall, the simulated oral human pharmacokinetic profiles showed good agreement with observed clinical data, and the model predicted that the C(max) , AUC, t(½) , V(z) /F and CL/F values were within 1.3-fold of the observed values. The absolute oral bioavailability of nilotinib in healthy humans was predicted to be low (< 25%).

Preclinical disposition of GDC-0973 and prospective and retrospective analysis of human dose and efficacy predictions

[3,4-Difluoro-2-(2-fluoro-4-iodo-phenylamino)-phenyl]-((S)-3-hydroxy-3-piperidin-2-yl-azetidin-1-yl)-methanone (GDC-0973) is a potent and highly selective inhibitor of mitogen-activated protein kinase(MAPK)/extracellular signal-regulated kinase (ERK) 1/2 (MEK1/2), a MAPK kinase that activates ERK1/2. The objectives of these studies were to characterize the disposition of GDC-0973 in preclinical species and to determine the relationship of GDC-0973 plasma concentrations to efficacy in Colo205 mouse xenograft models. The clearance (CL) of GDC-0973 was moderate in mouse (33.5 ml · min(-1) · kg(-1)), rat (37.9 ± 7.2 ml · min(-1) · kg(-1)), and monkey (29.6 ± 8.5 ml · min(-1) · kg(-1)). CL in dog was low (5.5 ± 0.3 ml · min(-1) · kg(-1)). The volume of distribution across species was large, 6-fold to 15-fold body water; half-lives ranged from 4 to 13 h. Protein binding in mouse, rat, dog, monkey, and human was high, with percentage unbound, 1 to 6%. GDC-0973-related radioactivity was rapidly and extensively distributed to tissues; however, low concentrations were observed in the brain. In rats and dogs, [(14)C]GDC-0973 was well absorbed (fraction absorbed, 70-80%). The majority of [(14)C]GDC-0973-related radioactivity was recovered in the bile of rat (74-81%) and dog (65%). The CL and volume of distribution of GDC-0973 in human, predicted by allometry, was 2.9 ml · min(-1) · kg(-1) and 9.9 l/kg, respectively. The predicted half-life was 39 h. To characterize the relationship between plasma concentration of GDC-0973 and tumor growth inhibition, pharmacokinetic-pharmacodynamic modeling was applied using an indirect response model. The KC(50) value for tumor growth inhibition in Colo205 xenografts was estimated to be 0.389 μM, and the predicted clinical efficacious dose was ∼10 mg. Taken together, these data are useful in assessing the disposition of GDC-0973, and where available, comparisons with human data were made.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 3: comparative assessement of prediction methods of human clearance

The objective of this study was to evaluate the performance of various allometric and in vitro-in vivo extrapolation (IVIVE) methodologies with and without plasma protein binding corrections for the prediction of human intravenous (i.v.) clearance (CL). The objective was also to evaluate the IVIVE prediction methods with animal data. Methodologies were selected from the literature. Pharmaceutical Research and Manufacturers of America member companies contributed blinded datasets from preclinical and clinical studies for 108 compounds, among which 19 drugs had i.v. clinical pharmacokinetics data and were used in the analysis. In vivo and in vitro preclinical data were used to predict CL by 29 different methods. For many compounds, in vivo data from only two species (generally rat and dog) were available and/or the required in vitro data were missing, which meant some methods could not be properly evaluated. In addition, 66 methods of predicting oral (p.o.) area under the curve (AUCp.o. ) were evaluated for 107 compounds using rational combinations of i.v. CL and bioavailability (F), and direct scaling of observed p.o. CL from preclinical species. Various statistical and outlier techniques were employed to assess the predictability of each method. Across methods, the maximum success rate in predicting human CL for the 19 drugs was 100%, 94%, and 78% of the compounds with predictions falling within 10-fold, threefold, and twofold error, respectively, of the observed CL. In general, in vivo methods performed slightly better than IVIVE methods (at least in terms of measures of correlation and global concordance), with the fu intercept method and two-species-based allometry (rat-dog) being the best performing methods. IVIVE methods using microsomes (incorporating both plasma and microsomal binding) and hepatocytes (not incorporating binding) resulted in 75% and 78%, respectively, of the predictions falling within twofold error. IVIVE methods using other combinations of binding assumptions were much less accurate. The results for prediction of AUCp.o. were consistent with i.v. CL. However, the greatest challenge to successful prediction of human p.o. CL is the estimate of F in human. Overall, the results of this initiative confirmed predictive performance of common methodologies used to predict human CL.

PhRMA CPCDC initiative on predictive models of human pharmacokinetics, part 1: goals, properties of the PhRMA dataset, and comparison with literature datasets

This study is part of the Pharmaceutical Research and Manufacturers of America (PhRMA) initiative on predictive models of efficacy, safety, and compound properties. The overall goal of this part was to assess the predictability of human pharmacokinetics (PK) from preclinical data and to provide comparisons of available prediction methods from the literature, as appropriate, using a representative blinded dataset of drug candidates. The key objectives were to (i) appropriately assemble and blind a diverse dataset of in vitro, preclinical in vivo, and clinical data for multiple drug candidates, (ii) evaluate the dataset with empirical and physiological methodologies from the literature used to predict human PK properties and plasma concentration-time profiles, (iii) compare the predicted properties with the observed clinical data to assess the prediction accuracy using routine statistical techniques and to evaluate prediction method(s) based on the degree of accuracy of each prediction method, and (iv) compile and summarize results for publication. Another objective was to provide a mechanistic understanding as to why one methodology provided better predictions than another, after analyzing the poor predictions. A total of 108 clinical lead compounds were collected from 12 PhRMA member companies. This dataset contains intravenous (n = 19) and oral pharmacokinetic data (n = 107) in humans as well as the corresponding preclinical in vitro, in vivo, and physicochemical data. All data were blinded to protect the anonymity of both the data and the company submitting the data. This manuscript, which is the first of a series of manuscripts, summarizes the PhRMA initiative and the 108 compound dataset. More details on the predictability of each method are reported in companion manuscripts.