Prediction Of Human Pharmacokinetics Research Articles

Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage.

We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.

Prediction of human serum concentration–time profiles of therapeutic monoclonal antibodies using common marmosets (Callithrix jacchus): initial assessment with canakinumab, adalimumab, and bevacizumab

Cynomolgus monkeys and human FcRn transgenic mice are generally used for pharmacokinetic predictions of therapeutic monoclonal antibodies (mAbs). In the present study, the application of the common marmoset, a small nonhuman primate, as a potential animal model for prediction was evaluated for the first time. Canakinumab, adalimumab, and bevacizumab, which exhibited linear pharmacokinetics in humans, were selected as the model compounds. Marmoset pharmacokinetic data were reportedly available only for canakinumab, and those for adalimumab and bevacizumab were acquired in-house. Four pharmacokinetic parameters for a two-compartment model (i.e. clearance and volume of distribution in the central and peripheral compartments) in marmosets were extrapolated to the values in humans with allometric scaling using the average exponents of the three mAbs. As a result, the observed human serum concentration–time curves of the three mAbs following intravenous administration and those of canakinumab and adalimumab following subcutaneous injections (with an assumed absorption rate constant and bioavailability) were reasonably predicted. Although further prediction studies using a sufficient number of other mAbs are necessary to evaluate the versatility of this model, the findings indicate that marmosets can be an alternative to preceding animals for human pharmacokinetic predictions of therapeutic mAbs.

Human Pharmacokinetic and CYP3A Drug-Drug Interaction Prediction of GDC-2394 Using Physiologically Based Pharmacokinetic Modeling and Biomarker Assessment.

Physiologically based pharmacokinetic (PBPK) modeling was used to predict the human pharmacokinetics and drug-drug interaction (DDI) of GDC-2394. PBPK models were developed using in vitro and in vivo data to reflect the oral and intravenous PK profiles of mouse, rat, dog, and monkey. The learnings from preclinical PBPK models were applied to a human PBPK model for prospective human PK predictions. The prospective human PK predictions were within 3-fold of the clinical data from the first-in-human study, which was used to optimize and validate the PBPK model and subsequently used for DDI prediction. Based on the majority of PBPK modeling scenarios using the in vitro CYP3A induction data (mRNA and activity), GDC-2394 was predicted to have no-to-weak induction potential at 900 mg twice daily (BID). Calibration of the induction mRNA and activity data allowed for the convergence of DDI predictions to a narrower range. The plasma concentrations of the 4β-hydroxycholesterol (4β-HC) were measured in the multiple ascending dose study to assess the hepatic CYP3A induction risk. There was no change in plasma 4β-HC concentrations after 7 days of GDC-2394 at 900 mg BID. A dedicated DDI study found that GDC-2394 has no induction effect on midazolam in humans, which was reflected by the totality of predicted DDI scenarios. This work demonstrates the prospective utilization of PBPK for human PK and DDI prediction in early drug development of GDC-2394. PBPK modeling accompanied with CYP3A biomarkers can serve as a strategy to support clinical pharmacology development plans. SIGNIFICANCE STATEMENT: This work presents the application of physiologically based pharmacokinetic modeling for prospective human pharmacokinetic (PK) and drug-drug interaction (DDI) prediction in early drug development. The strategy taken in this report represents a framework to incorporate various approaches including calibration of in vitro induction data and consideration of CYP3A biomarkers to inform on the overall CYP3A-related DDI risk of GDC-2394.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration–time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration–time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

Effects of genetic polymorphisms of canine CYP2D15 on in vitro hepatic metabolism and in vivo pharmacokinetics of celecoxib, and the strategy for the accurate prediction of human pharmacokinetics

Effects of genetic polymorphisms of canine CYP2D15 on in vitro hepatic metabolism and in vivo pharmacokinetics of celecoxib, and the strategy for the accurate prediction of human pharmacokinetics

In silico modeling and simulation of organ-on-a-chip systems to support data analysis and a priori experimental design.

Organ-on-a-chip (OoC) systems are a promising new class of invitro devices that can combine various tissues, cultured in different compartments, linked by media flow. The properties of these novel invitro systems linked to increased physiological relevance of culture conditions may lead to more invivo-relevant cell phenotypes, enabling better invitro pharmacology and toxicology assessment. Improved cell activities combined with longer lasting cultures offer opportunities to improve the characterization of absorption, distribution, metabolism, and excretion (ADME) processes, potentially leading to more accurate prediction of human pharmacokinetics (PKs). The inclusion of barrier tissue elements and metabolically competent tissue types results in complex concentration-time profiles (in vitro PK) for test drugs and their metabolites that require appropriate mathematical modeling of invitro data for parameter estimation. In particular, modeling is critical to estimate invitro ADME parameters when multiple different tissues are combined in a single device. Therefore, sophisticated in silico data analysis and a priori experimental design are highly recommended for OoC experiments in a manner not needed with standard ADME screening. The design of the experiment should be optimized based on an investigation of the structural characteristics of the invitro system, the ADME features of the test compound and any available knowledge of cell phenotypes. This tutorial aims to provide such a modeling framework to inform experimental design and refine parameter estimation in a Gut-Liver OoC (the most studied multi-organ systems to predict the oral drug PKs) to improve translatability of data generated in such complex cellular systems.

Abstract A139: Preclinical characterization and prediction of human pharmacokinetics and efficacious dose for VIP236, a novel alpha V beta 3 binding small molecule-drug conjugate (SMDC)

Abstract VIP236 is a small molecule drug conjugate (SMDC) consisting of an alpha v beta 3 (αvβ3) integrin binder linked to an optimized camptothecin (optCPT) payload via a protease sensitive peptide sequence. The overall drug design strategy for VIP236 is targeted delivery to the tumor microenvironment (TME) with extracellular release of the opCPT by neutrophil elastase (NE) present in the TME. VIP236 is anticipated to kill tumor cells while reducing effects to normal tissue. VIP236 was characterized in preclinical pharmacokinetic (PK) and efficacy studies to predict its human PK and efficacious dose. Briefly, VIP236 was administered intravenously (IV) to mice, rats, and dogs to characterize both its in vivo PK (1-2 mg/kg) and its routes of excretion (bile-duct cannulated [BDC] rat at 10 mg/kg). VIP236 was evaluated in vitro for its protein binding characteristics and as a substrate of human drug transporters. In addition, VIP236 was dosed to MX-1 (breast cancer) xenografted mice at doses ranging from 26-40 mg/kg IV on a 3 days on/4 days off regimen to characterize its antitumor activity. VIP236 had low plasma clearance in mice, rats, and dogs (CL=2.7-4.4 mL/min/kg) and low volume of distribution (Vss = 0.11 to 0.25 L/kg). Terminal half-life ranged from 0.93 hour in mice to 3.07 hours in dogs. In BDC rats, VIP236 was primarily excreted in bile with biliary clearance accounting for essentially all the total body CL. There was minimal to no metabolites in rat bile and urine. In vitro VIP236 exhibited low permeability in MDCKII cell monolayers and was identified as a substrate of BSEP and MRP2. VIP236 was highly protein bound to plasma protein with fraction unbound being <5% in all species tested. VIP236 showed robust tumor regression in MX-1 xenografted mice at all doses tested. Allometric methods were used to predict a low human VIP236 CL (0.87-2.68 mL/min/kg) and Vss (0.10 L/kg). Translational pharmacokinetic/pharmacodynamic modeling was used to predict human efficacious doses and exposures by benchmarking VIP236 against irinotecan antitumor activity in MX-1 xenograft mice after 5 cycles of dosing. Based on this analysis, predicted human doses ranged from 1-5 mg/kg IV on a 2 days on/5 days off regimen. VIP236 is currently being evaluated in a first-in-human study in patients with advanced or metastatic solid tumors (NCT05712889). Citation Format: Harvey Wong, Beatrix Stelte-Ludwig, Hans-Georg Lerchen, Amy J Johnson, Melanie M Frigault, Raquel Izumi, Ahmed Hamdy. Preclinical characterization and prediction of human pharmacokinetics and efficacious dose for VIP236, a novel alpha V beta 3 binding small molecule-drug conjugate (SMDC) [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A139.

Abstract C101: Preclinical and clinical pharmacokinetics (PK) of NBF-006, a novel siRNA inhibitor of glutathione-s-transferase P (GSTP) encapsulated in a lipid nanoparticle (LNP), for treatment of advanced non-small cell lung cancer (NSCLC)

Abstract Background: GSTP has an important role in detoxification and anti-oxidative damage response. Additionally, GSTP has a chaperone function that regulates multiple key oncogenic pathways such as KRAS and JNK. Since alternative or redundant pathways could compensate for the inhibition of a single kinase, targeting multiple pathways could lead to more effective therapy. NBF-006 is a drug product comprised of GSTP siRNA encapsulated within a novel LNP. It is designed to deliver siRNA to localized and metastatic lung tumors. Unlike other well-established drug modalities, there is a general need for increased understanding of the PK of RNA therapeutics. For NBF-006, we present preclinical PK data in healthy mice and monkeys, prediction of human doses, and phase 1 clinical PK data. Methods: Preclinical PK studies were performed by intravenous bolus injection of NBF-006 once weekly for 1 month at 5, 20, and 40 mg/kg in mice and 6, 12, and 18 mg/kg in cynomolgus monkeys. Human drug exposures were predicted based upon simple allometry. For the phase 1 clinical study, a total of 48 advanced NSCLC, pancreatic, or colorectal cancer patients received 4 weekly doses of NBF-006 by infusion at 0.15, 0.3, 0.6, 1.2 or 1.6 mg/kg followed by a 2-week rest period. In all studies, serial blood samples were collected at weeks 1 and 4. Plasma concentrations of siRNA were determined by a hybridization ELISA. Non-compartmental PK analysis (NCA) was performed with the resulting concentration-time data. Results: In mice, terminal elimination half-life (t1/2) of plasma siRNA ranged between 3.69 to 9.04 hr, clearance (CL) ranged between 3.61 and 5.81 mL/hr/kg, and volume of distribution (Vd) ranged between 23.5 and 57.4 mL/kg. In cynomolgus monkeys, t1/2 ranged between 8.22 and 28.2 hr, CL ranged between 1.38 and 3.97 mL/hr/kg, and Vd ranged between 31.7 to 83.3 mL/kg. Exposure (maximal plasma concentration, Cmax, and area under the curve, AUC) increased with increasing dose in both species and was generally dose proportional. Following weekly administration of NBF-006, no apparent accumulation or reduction of siRNA exposure was observed. Human PK exposures were well-predicted within 2-fold accuracy at clinically studied doses. Clinically, NCA demonstrated consistent slow CL and low Vd with a mean t1/2 of 41 (± 13) hours. Exposure showed dose proportionality from 0.15 to 1.2 mg/kg. At 1.6 mg/kg, exposure was not statistically different than that of 1.2 mg/kg. No accumulation risk potential was identified following the current dosing regimen. Moreover, exposure was not reduced after repeat dosing in any dose group. Conclusions: Preclinical PK profiling enabled initial characterization of the in vivo disposition of NBF-006 and prediction of human PK and doses. In patients, repeated administration of NBF-006 was well-tolerated up to 1.6 mg/kg. Exposure appeared to be dose proportional through 1.2 mg/kg. Neither exposure reduction nor accumulation was observed. Citation Format: Michael Hall, Zhihong O'Brien, Sonya Zabludoff. Preclinical and clinical pharmacokinetics (PK) of NBF-006, a novel siRNA inhibitor of glutathione-s-transferase P (GSTP) encapsulated in a lipid nanoparticle (LNP), for treatment of advanced non-small cell lung cancer (NSCLC) [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C101.

Predictability of human exposure by human-CYP3A4-transgenic mouse models: A meta-analysis.

First-in-human dose predictions are primarily based on no-observed-adverse-effect levels in animal studies. Predictions from these animal models are only as effective as their ability to predict human results. To narrow the gap between human and animals, researchers have, among other things, focused on the replacement of animal cytochrome P450 (CYP) enzymes with their human counterparts (called humanization), especially in mice. Whereas research in humanized mice is extensive, the emphasis has been particularly on qualitative rather than quantitative predictions. Because the CYP3A4 enzyme is most involved in the metabolism of clinically used drugs, most benefit was expected from CYP3A4 models. There are several applications of these mouse models regarding invivo CYP3A4 functionality, one of which might be their capacity to help improve first-in-human (FIH) dose predictions for CYP3A4-metabolized drugs. To evaluate whether human-CYP3A4-transgenic mouse models are better predictors of human exposure compared to the wild-type mouse model, we performed a meta-analysis comparing both mouse models in their ability to accurately predict human exposure of small-molecule drugs metabolized by CYP3A4. Results showed that, in general, the human-CYP3A4-transgenic mouse model had similar accuracy in the prediction of human exposure compared to the wild-type mouse model, suggesting that there is limited added value in humanization of the mouse Cyp3a enzymes if the primary aim is to acquire more accurate FIH dose predictions. Despite the results of this meta-analysis, corrections for interspecies differences through extension of human-CYP3A4-transgenic mouse models with pharmacokinetic modeling approaches seems a promising contribution to more accurate quantitative predictions of human pharmacokinetics.

Prediction of Human Pharmacokinetics From Chemical Structure: Combining Mechanistic Modeling with Machine Learning

Pharmacokinetics (PK) is the result of a complex interplay between compound properties and physiology, and a detailed characterization of a molecule's PK during preclinical research is key to understanding the relationship between applied dose, exposure, and pharmacological effect. Predictions of human PK based on the chemical structure of a compound are highly desirable to avoid advancing compounds with unfavorable properties early on and to reduce animal testing, but data to train such models are scarce. To address this problem, we combine well-established Physiologically Based Pharmacokinetic models with Deep Learning models for molecular property prediction into a hybrid model to predict PK parameters for small molecules directly from chemical structure. Our model predicts exposure after oral and intravenous administration with fold change errors of 1.87 and 1.86, respectively, in healthy subjects and 2.32 and 2.23, respectively, in patients with various diseases. Unlike pure Deep Learning models, the hybrid model can predict endpoints on which it was not trained. We validate this extrapolation capability by predicting full concentration-time profiles for compounds with published PK data. Our model enables early selection and prioritization of the most promising drug candidates, which can lead to a reduction in animal testing during drug discovery and development.

The Comparison of Machine Learning and Mechanistic In Vitro-In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance.

Accurate prediction of human pharmacokinetics (PK) remains one of the key objectives of drug metabolism and PK (DMPK) scientists in drug discovery projects. This is typically performed by using in vitro-in vivo extrapolation (IVIVE) based on mechanistic PK models. In recent years, machine learning (ML), with its ability to harness patterns from previous outcomes to predict future events, has gained increased popularity in application to absorption, distribution, metabolism, and excretion (ADME) sciences. This study compares the performance of various ML and mechanistic models for the prediction of human IV clearance for a large (645) set of diverse compounds with literature human IV PK data, as well as measured relevant in vitro end points. ML models were built using multiple approaches for the descriptors: (1) calculated physical properties and structural descriptors based on chemical structure alone (classical QSAR/QSPR); (2) in vitro measured inputs only with no structure-based descriptors (ML IVIVE); and (3) in silico ML IVIVE using in silico model predictions for the in vitro inputs. For the mechanistic models, well-stirred and parallel-tube liver models were considered with and without the use of empirical scaling factors and with and without renal clearance. The best ML model for the prediction of in vivo human intrinsic clearance (CLint) was an in vitro ML IVIVE model using only six in vitro inputs with an average absolute fold error (AAFE) of 2.5. The best mechanistic model used the parallel-tube liver model, with empirical scaling factors resulting in an AAFE of 2.8. The corresponding mechanistic model with full in silico inputs achieved an AAFE of 3.3. These relative performances of the models were confirmed with the prediction of 16 Pfizer drug candidates that were not part of the original data set. Results show that ML IVIVE models are comparable to or superior to their best mechanistic counterparts. We also show that ML IVIVE models can be used to derive insights into factors for the improvement of mechanistic PK prediction.

Novel modeling approach integrating population pharmacokinetics and interspecies scaling to predict human pharmacokinetics of the new anti-tuberculosis agent telacebec (Q203)

Telacebec is a new anti-tuberculosis agent with promising therapeutic activity and a favorable safety profile. This study aimed to characterize the pharmacokinetics of telacebec via interspecies scaling and population pharmacokinetic modeling for the prediction of human pharmacokinetics. Preclinical pharmacokinetic data were obtained from mice, rats, and dogs following intravenous and oral doses of telacebec. A population pharmacokinetic model was developed to describe the pharmacokinetic data from all three species. The disposition parameters were well correlated with the body weight for all species using an allometric equation. Thus, the allometric scaling was incorporated into the population pharmacokinetic model, which could simultaneously describe the plasma concentration vs. time data from all preclinical studies as well as the Phase 1A clinical study. The developed model was used to predict the pharmacokinetics of telacebec after IV injection, including the clearance (CL) of 168.58 [118.86 – 238.73] mL/min and volume of distribution (Vss) of 968.84 [396.87 – 2831.31] L for 80-kg human. The absolute bioavailability of telacebec in humans in the fed state was estimated as 70.34 ± 9.91%. Finally, the population pharmacokinetic model with allometric scaling was utilized to simulate the plasma concentration vs. time profiles of telacebec after multiple oral doses in humans. The model-predicted profiles well agreed with the observed data in Phase 1B clinical trial. The present pharmacokinetic model may help better understand the activity of telacebec, leading to the design of optimal dosing regimens and new formulation development.

A Quantitative Prediction Method for the Human Pharmacokinetics of Fc-Fusion Proteins.

Fc fusion is an effective strategy for extending the half-lives of therapeutic proteins. This study aimed to evaluate the applicability of a human pharmacokinetics prediction method for Fc-fusion proteins by extending on reported methods for monoclonal antibodies (mAbs). To predict human pharmacokinetic profiles following intravenous (IV) dosing, the pharmacokinetic data for 11 Fc-fusion proteins in monkeys were analysed by two approaches: a species-invariant time method with a range of allometric exponents in clearance (CL, 0.7-1.0) and a two-compartment model reported for mAbs. The pharmacokinetic profiles following subcutaneous (SC) dosing were predicted by simple dose normalisation from monkeys or using the geometric means of the absorption rate constant (Ka) and bioavailability (BA) for mAbs or Fc-fusion proteins in humans and compared. In the case of IV administration, the area under the curve could be predicted for more than 85% of Fc-fusion proteins within a twofold difference from the observed value using the species-invariant time method (scaling exponent for CL, 0.95). For SC dosing, incorporating the geometric means of absorption parameters for both mAbs (BA 68.2%, Ka 0.287 day-1) and Fc-fusion proteins (BA 63.0%, Ka 0.209 day-1) in humans provided better accuracy than simple normalisation from monkeys. We have successfully predicted the human pharmacokinetic profiles of Fc-fusion proteins for both IV and SC administration within twofold of the observed value from monkey pharmacokinetic data by extending on reported methods for mAbs. This method will facilitate drug discovery and development of Fc-fusion proteins.

Prediction of Human Pharmacokinetics of Phosphorodiamidate Morpholino Oligonucleotides in Duchenne Muscular Dystrophy Patients Using Viltolarsen.

Several modified antisense oligonucleotides (ASOs) have recently been approved for clinical use. Some are phosphorodiamidate morpholino oligomers (PMOs), which, unlike other nucleic acids, are not negatively charged. Thus, PMOs differ from other ASOs in their pharmacokinetic (PK) properties. Drugs with a PMO backbone have been administered to Duchenne muscular dystrophy pediatric patients; however, appropriate methodologies are not currently available to predict their human PK from nonclinical data. In this study, we used viltolarsen as a representative PMO to investigate the applicability of the allometric scaling approach to human PK prediction. We first summarized the nonclinical and clinical PK data for viltolarsen as showing high total clearance, low serum protein binding, metabolic resistance, and urinary excretion as the unchanged drug in both animals and humans. We then investigate the PK of viltolarsen in mice, rats, cynomolgus monkeys, and dogs and used the results, with body weight, to extrapolate to humans by several methods. The estimate of human total clearance obtained from cynomolgus monkeys was the best, and body weight may be the key factor in accurately predicting human total clearance. In contrast, all of the well-known prediction methods for the volume of distribution at steady state gave underestimates. However, the human PK profiles predicted from the PK parameters in cynomolgus monkeys fit the observed human plasma concentrations well. These results are expected to contribute to the further development of PMOs. SIGNIFICANCE STATEMENT: We investigated how to predict the human PK of phosphorodiamidate morpholino oligomers from nonclinical data. The estimates of human PK parameters and profiles determined from cynomolgus monkeys by an allometric scaling approach were the most suitable, and the cynomolgus monkey body weight may be the key factor in accurately predicting human total clearance.

Generation of cynomolgus monkey airway, liver ductal, and kidney organoids with pharmacokinetic functions

Over the past decades, the pre-clinical evaluation of new drugs requires toxicological screening in animal models. The development of non-animal and nonclinical screening models could potentially play a role in the prediction of human pharmacokinetics of new drug candidates. In this study, we established stable organoids of the cynomolgus monkey airway, liver ductal, and kidney that could be passaged and cryopreserved. Drug sensitivity analyses revealed that very low doses of gemcitabine and 5-fluorouridine were toxic to the airway, liver ductal, and kidney organoids. Only high doses of regorafenib were toxic to liver ductal organoids while airway organoids were resistant to all doses of pemetrexed. These organoids showed tissue-specific expression of drug-metabolizing enzymes and drug transporter genes with liver ductal organoids exhibiting the most significant expression of all drug-metabolizing enzymes and transporters. The systematic evaluation of the pharmacokinetic functions of the cynomolgus monkey kidney, liver ductal, and airway organoids could find application in the pre-clinical toxicological studies of new drugs.

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

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

Industry Perspective on the Pharmacokinetic and ADME Characterization of Heterobifunctional Protein Degraders.

Targeted protein degraders (TPDs), specifically the bifunctional protein degraders discussed in this manuscript, consist of two linked ligands for a protein of interest and an E3 ligase, resulting in molecules which largely violate accepted physicochemical limits (e.g. Lipinski's Rule of 5) for oral bioavailability. In 2021, the IQ Consortium Degrader DMPK/ADME Working Group undertook a survey of eighteen IQ member and non-member companies working on degraders to understand whether the characterization and optimization of these molecules were different from any other beyond the Rule of 5 (bRo5) compounds. Additionally, the working group sought to identify PK/ADME areas in need of further evaluation and where additional tools could aid in more rapid advancement of TPDs to patients. The survey revealed that although TPDs reside in a challenging bRo5 physicochemical space, most respondents focus their efforts on oral delivery. Physicochemical properties required for oral bioavailability were generally consistent across the companies surveyed. Many of the member companies used modified assays to address challenging degrader properties (e.g. solubility, non-specific binding), but only half indicated that they modified their drug discovery workflows. The survey also suggested the need for further scientific investigation in the areas of CNS penetration, active transport, renal elimination, lymphatic absorption, in silico/machine learning, and human pharmacokinetic prediction. Based on the survey results, the Degrader DMPK/ADME Working Group concludes that TPD evaluation does not fundamentally differ from other bRo5 compounds, but requires some modification compared to traditional small molecules and proposes a generic workflow for PK/ADME evaluation of bifunctional TPDs. Significance Statement Based on an industry survey, this article provides an understanding of the current state of ADME science pertaining to characterizing and optimizing targeted protein degraders, specifically bifunctional protein degraders, based upon the responses provided by 18 IQ consortium members and non-members developing targeted protein degraders to treat disease. Additionally, this article puts into context the differences / similarities in methods and strategies utilized for heterobifunctional protein degraders compared to other bRo5 molecules and those used for conventional small molecule drugs.

Gene expression databases for physiologically based pharmacokinetic modeling of humans and animal species.

In drug research, developing a sound understanding of the key mechanistic drivers of pharmacokinetics (PK) for new molecular entities is essential for human PK and dose predictions. Here, characterizing the absorption, distribution, metabolism, and excretion (ADME) processes is crucial for a mechanistic understanding of the drug-target and drug-body interactions. Sufficient knowledge on ADME processes enables reliable interspecies and human PK estimations beyond allometric scaling. The physiologically based PK (PBPK) modeling framework allows the explicit consideration of organ-specific ADME processes. The sum of all passive and active ADME processes results in the observed plasma PK. Gene expression information can be used as surrogate for protein abundance and activity within PBPK models. The absolute and relative expression of ADME genes can differ between species and strains. This is affecting both, the PK and pharmacodynamics and is therefore posing a challenge for the extrapolation from preclinical findings to humans. We developed an automated workflow that generates whole-body gene expression databases for humans and other species relevant in drug development, animal health, nutritional sciences, and toxicology. Solely, bulk RNA-seq data curated and provided by the Swiss Institute of Bioinformatics from healthy, normal, and untreated primary tissue samples were considered as an unbiased reference of normal gene expression. The databases are interoperable with the Open Systems Pharmacology Suite (PK-Sim and MoBi) and enable seamless access to a central source of curated cross-species gene expression data. This will increase data transparency, increase reliability and reproducibility of PBPK model simulations, and accelerate mechanistic PBPK model development in the future.

In Silico Prediction of Human Clinical Pharmacokinetics with ANDROMEDA by Prosilico: Predictions for an Established Benchmarking Data Set, a Modern Small Drug Data Set, and a Comparison with Laboratory Methods

There is an ongoing aim to replace animal and in vitro laboratory models with in silico methods. Such replacement requires the successful validation and comparably good performance of the alternative methods. We have developed an in silico prediction system for human clinical pharmacokinetics, based on machine learning, conformal prediction and a new physiologically-based pharmacokinetic model, i.e. ANDROMEDA. The objectives of this study were: a) to evaluate how well ANDROMEDA predicts the human clinical pharmacokinetics of a previously proposed benchmarking data set comprising 24 physicochemically diverse drugs and 28 small drug molecules new to the market in 2021; b) to compare its predictive performance with that of laboratory methods; and c) to investigate and describe the pharmacokinetic characteristics of the modern drugs. Median and maximum prediction errors for the selected major parameters were ca 1.2 to 2.5-fold and 16-fold for both data sets, respectively. Prediction accuracy was on par with, or better than, the best laboratory-based prediction methods (superior performance for a vast majority of the comparisons), and the prediction range was considerably broader. The modern drugs have higher average molecular weight than those in the benchmarking set from 15 years earlier (ca 200 g/mol higher), and were predicted to (generally) have relatively complex pharmacokinetics, including permeability and dissolution limitations and significant renal, biliary and/or gut-wall elimination. In conclusion, the results were overall better than those obtained with laboratory methods, and thus serve to further validate the ANDROMEDA in silico system for the prediction of human clinical pharmacokinetics of modern and physicochemically diverse drugs.

Human pharmacokinetics prediction with an in vitro–in vivo correction factor approach and in vitro drug-drug interaction profile of bictegravir, a potent integrase-strand transfer inhibitor component in approved biktarvy® for the treatment of HIV-1 infection

Bictegravir (BIC) is a potent small-molecule integrase strand-transfer inhibitor (INSTI) and a component of Biktarvy®, a single-tablet combination regimen that is currently approved for the treatment of human immunodeficiency virus type 1 (HIV-1) infection. The in vitro properties, pharmacokinetics (PK), and drug-drug interaction (DDI) profile of BIC were characterised in vitro and in vivo. BIC is a weakly acidic, ionisable, lipophilic, highly plasma protein-bound BCS class 2 molecule, which makes it difficult to predict human PK using standard methods. Its systemic plasma clearance is low, and the volume of distribution is approximately the volume of extracellular water in nonclinical species. BIC metabolism is predominantly mediated by cytochrome P450 enzyme (CYP) 3A and UDP-glucuronosyltransferase 1A1. BIC shows a low potential to perpetrate clinically meaningful DDIs via known drug metabolising enzymes or transporters. The human PK of BIC was predicted using a combination of bioavailability and volume of distribution scaled from nonclinical species and a modified in vitro-in vivo correlation (IVIVC) correction for clearance. Phase 1 studies in healthy subjects largely bore out the prediction and supported the methods used. The approach presented herein could be useful for other drug molecules where standard projections are not sufficiently accurate.