Predicting Clearance Mechanism in Drug Discovery: Extended Clearance Classification System (ECCS)

Considerations from an International Regulatory and Pharmaceutical Industry (IQ MPS Affiliate) Workshop on the Standardization of Complex In Vitro Models in Drug Development

In May 2022, there is an International Regulatory and Pharmaceutical Industry (Innovation and Quality [IQ] Microphysiological Systems [MPS] Affiliate) Workshop on the standardization of complex in vitro models (CIVMs) in drug development. This manuscript summarizes the discussions and conclusions of this joint workshop organized and executed by the IQ MPS Affiliate and the United States Food and Drug Administration (FDA). A key objective of the workshop is to facilitate discussions around opportunities and/or needs for standardization of MPS and chart potential pathways to increase model utilization in the context of regulatory decision making. Participation in the workshop included 200 attendees from the FDA, IQ MPS Affiliate, and 26 global regulatory organizations and affiliated parties representing Europe, Japan, and Canada. It is agreed that understanding global perspectives regarding the readiness of CIVM/MPS models for regulatory decision making and potential pathways to gaining acceptance is useful to align on globally. The obstacles are currently too great to develop standards for every context of use (COU). Instead, it is suggested that a more tractable approach may be to think of broadly applicable standards that can be applied regardless of COU and/or organ system. Considerations and next steps for this effort are described.

In vivo deposition of poorly soluble drugs

Administered drug molecules, whether dissolved or solubilized, have the potential to precipitate and accumulate as solid forms in tissues and cells within the body. This phase transition can significantly impact the pharmacokinetics of treatment. It is thus crucial to gain an understanding of how drug solubility/permeability, drug formulations and routes of administration affect in vivo behaviors of drug deposition. This review examines literature reports on the drug deposition in tissues and cells of poorly water-soluble drugs, as well as underlying physical mechanisms that lead to precipitation. Our work particularly highlights drug deposition in macrophages and the subcellular fate of precipitated drugs. We also propose a tissue permeability-based classification framework to evaluate precipitation potentials of poorly soluble drugs in major organs and tissues. The impact on pharmacokinetics is further discussed and needs to be considered in developing drug delivery systems. Finally, bioimaging techniques that are used to examine aggregated states and the intracellular trafficking of absorbed drugs are summarized.

Evaluation of 14 PFAS for permeability and organic anion transporter interactions: Implications for renal clearance in humans

Per- and polyfluoroalkyl substances (PFAS) encompass a diverse group of synthetic fluorinated chemicals known to elicit adverse health effects in animals and humans. However, only a few studies investigated the mechanisms underlying clearance of PFAS. Herein, the relevance of human renal transporters and permeability to clearance and bioaccumulation for 14 PFAS containing three to eleven perfluorinated carbon atoms (ηpfc = 3-11) and several functional head-groups was investigated. Apparent permeabilities and interactions with human transporters were measured using in vitro cell-based assays, including the MDCK-LE cell line, and HEK293 stable transfected cell lines expressing organic anion transporter (OAT) 1-4 and organic cation transporter (OCT) 2. The results generated align with the Extended Clearance Classification System (ECCS), affirming that permeability, molecular weight, and ionization serve as robust predictors of clearance and renal transporter engagement. Notably, PFAS with low permeability (ECCS 3A and 3B) exhibited substantial substrate activity for OAT1 and OAT3, indicative of active renal secretion. Furthermore, we highlight the potential contribution of OAT4-mediated reabsorption to the renal clearance of PFAS with short ηpfc, such as perfluorohexane sulfonate (PFHxS). Our data advance our mechanistic understanding of renal clearance of PFAS in humans, provide useful input parameters for toxicokinetic models, and have broad implications for toxicological evaluation and regulatory considerations.

Transporter-mediated drug-drug interactions: regulatory guidelines, in vitro and in vivo methodologies and translation, special populations, and the blood-brain barrier

This review, part of a special issue on drug-drug interactions (DDIs) spearheaded by the International Society for the Study of Xenobiotics (ISSX) New Investigators, explores the critical role of drug transporters in absorption, disposition, and clearance in the context of DDIs. Over the past two decades, significant advances have been made in understanding the clinical relevance of these transporters. Current knowledge on key uptake and efflux transporters that affect drug disposition and development is summarized. Regulatory guidelines from the FDA, EMA, and PMDA that inform the evaluation of potential transporter-mediated DDIs are discussed in detail. Methodologies for preclinical and clinical testing to assess potential DDIs are reviewed, with an emphasis on the utility of physiologically based pharmacokinetic (PBPK) modeling. This includes the application of relative abundance and expression factors to predict human pharmacokinetics (PK) using preclinical data, integrating the latest regulatory guidelines. Considerations for assessing transporter-mediated DDIs in special populations, including pediatric, hepatic, and renal impairment groups, are provided. Additionally, the impact of transporters at the blood-brain barrier (BBB) on the disposition of CNS-related drugs is explored. Enhancing the understanding of drug transporters and their role in drug disposition and toxicity can improve efficacy and reduce adverse effects. Continued research is essential to bridge remaining gaps in knowledge, particularly in comparison with cytochrome P450 (CYP) enzymes.

Physiologically based kinetic (PBK) modeling of propiconazole using a machine learning-enhanced read-across approach for interspecies extrapolation

A significant challenge in the traditional human health risk assessment of agrochemicals is the uncertainty in quantifying the interspecies differences between animal models and humans. To work toward a more accurate and animal-free risk determination, new approaches such as physiologically based kinetic (PBK) modeling have been used to perform dosimetry extrapolation from animals to humans. However, the regulatory use and acceptance of PBK modeling is limited for chemicals that lack in vivo animal pharmacokinetic (PK) data, given the inability to evaluate models. To address these challenges, this study developed PBK models in the absence of in vivo PK data for the fungicide propiconazole, an activator of constitutive androstane receptor (CAR)/pregnane X receptor (PXR). A fit-for-purpose read-across approach was integrated with hierarchical clustering - an unsupervised machine learning algorithm, to bridge the knowledge gap. The integration allowed the incorporation of a broad spectrum of attributes for analog consideration, and enabled the analog selection in a simple, reproducible, and objective manner. The applicability was evaluated and demonstrated using penconazole (source) and three pseudo-unknown target chemicals (epoxiconazole, tebuconazole and triadimefon). Applying this machine learning-enhanced read-across approach, difenoconazole was selected as the most appropriate analog for propiconazole. A mouse PBK model was developed and evaluated for difenoconazole (source), with the mode of action of CAR/PXR activation incorporated to simulate the in vivo autoinduction of metabolism. The difenoconazole mouse model then served as a template for constructing the propiconazole mouse model. A parallelogram approach was subsequently applied to develop the propiconazole rat and human models, enabling a quantitative assessment of interspecies differences in dosimetry. This integrated approach represents a substantial advancement toward refining risk assessment of propiconazole within the framework of animal alternative safety assessment strategies.

A commentary on "Are all measures of liver Kpuu a function of FH, as determined following oral dosing, or have we made a critical error in defining hepatic drug clearance?"

The clearance concept has been used in pharmacokinetics for over 50 years. However, there is still much debate regarding mathematical clearance models. A recent article discussed that there is a critical error in a basic assumption that leads to the mechanistic hepatic clearance models (Benet, L.Z., Sodhi, J.K., 2024. Are all measures of liver Kpuu a function of FH, as determined following oral dosing, or have we made a critical error in defining hepatic drug clearance? European Journal of Pharmaceutical Sciences 196, 106,753. https://doi.org/10.1016/j.ejps.2024.106753). This commentary discusses this point based on the extended clearance model (ECM), which is increasingly used in modern drug discovery and development. Confusion about clearance can be avoided by using clearly defined drug concentrations based on hierarchical body structures.

The synthetic cathinones MDPHP and MDPV: Comparison of the acute effects in mice, in silico ADMET profiles and clinical reports

The 3,4-methylenedioxy-alpha-pyrrolidinohexanophenone (MDPHP) is a synthetic cathinone closely related to 3,4-methylenedioxypyrovalerone (MDPV), one of the most common synthetic cathinones present in the "bath salts". MDPHP has recently gained attention due to increasing seizures and involvement in human intoxications which occurred in Europe and Italy in the last years, but currently there is a lack of information about its pharmaco-toxicological effects. With the aim at filling this gap, the present study is endeavoured to (i) evaluate the effects of acute administration of MDPHP (0.01-20 mg/kg; i.p.) on behaviour, cardiorespiratory and cardiovascular parameters in CD-1 male mice, comparing them to those observed after administration of MDPV; (ii) predict the ADMET profile of the two analogues using the Plus ADMET Predictor®; (iii) present clinical data related to MDPHP and MDPV-induced intoxications recorded between 2011 and 2023 by the Pavia Poison Control Centre (PCC) - National Toxicology Information Centre (Istituti Clinici Scientifici Maugeri, IRCCS Pavia, Italy). Our results substantiated that MDPHP and MDPV similarly affect sensorimotor and behavioural responses in mice, importantly increased locomotion and induced aggressive behaviour, and, at higher dosage, increased heart rate and blood pressure. These findings are in line with those observed in humans, revealing severe toxidromes typically characterized by Central Nervous System (CNS) alterations (behavioural/neuropsychiatric symptoms), including psychomotor agitation and aggressiveness, cardiovascular and respiratory disorders (e.g. tachycardia, hypertension, dyspnoea), and other peripheral symptoms (e.g. hyperthermia, acidosis, rhabdomyolysis).

The Metabolism and Disposition of Brepocitinib in Humans and Characterization of the Formation Mechanism of an Aminopyridine Metabolite

Brepocitinib is an oral once-daily Janus kinase 1 and Tyrosine kinase 2 selective inhibitor currently in development for the treatment of several autoimmune disorders. Mass balance and metabolic profiles were determined using accelerator mass spectrometry in six healthy male participants following a single oral 60 mg dose of 14C-brepocitinib (∼300 nCi). The average mass balance recovery was 96.7% ± 6.3%, with the majority of dose (88.0% ± 8.0%) recovered in urine and 8.7% ± 2.1% of the dose recovered in feces. Absorption of brepocitinib was rapid, with maximal plasma concentrations of total radioactivity and brepocitinib achieved within 0.5 hours after dosing. Circulating radioactivity consisted primarily of brepocitinib (47.8%) and metabolite M1 (37.1%) derived from hydroxylation at the C5' position of the pyrazole ring. Fractional contributions to metabolism via cytochrome P450 enzymes were determined to be 0.77 for CYP3A4/5 and 0.14 for CYP1A2 based on phenotyping studies in human liver microsomes. However, additional clinical studies are required to understand the potential contribution of CYP1A1. Approximately 83% of the dose was eliminated as N-methylpyrazolyl oxidative metabolites, with 52.1% of the dose excreted as M1 alone. Notably, M1 was not observed as a circulating metabolite in earlier metabolic profiling of human plasma from a multiple ascending dose study with unlabeled brepocitinib. Mechanistic studies revealed that M1 was highly unstable in human plasma and phosphate buffer, undergoing chemical oxidation leading to loss of the 5-hydroxy-1-methylpyrazole moiety and formation of aminopyrimidine cleavage product M2. Time-dependent inhibition and trapping studies with M1 yielded insights into the mechanism of this unusual and unexpected instability. SIGNIFICANCE STATEMENT: This study provides a detailed understanding of the disposition and metabolism of brepocitinib, a JAK1/TYK2 inhibitor for atopic dermatitis, in humans as well as characterization of clearance pathways and pharmacokinetics of brepocitinib and its metabolites.

Mechanistic Determinants of Daprodustat Drug-Drug Interactions and Pharmacokinetics in Hepatic Dysfunction and Chronic Kidney Disease: Significance of OATP1B-CYP2C8 Interplay

Daprodustat is the first oral hypoxia-inducible factor prolyl hydroxylase inhibitor approved recently for the treatment of anemia caused by chronic kidney disease (CKD) in adults receiving dialysis. We evaluated the role of organic anion transporting polypeptide (OATP)1B-mediated hepatic uptake transport in the pharmacokinetics (PKs) of daprodustat using in vitro and in vivo studies, and physiologically-based PK (PBPK) modeling of its drug-drug interactions (DDIs) with inhibitor drugs. In vitro, daprodustat showed specific transport by OATP1B1/1B3 in the transfected cell systems and primary human and monkey hepatocytes. A single-dose oral rifampin (OATP1B inhibitor) reduced daprodustat intravenous clearance by a notable 9.9 ± 1.2-fold (P < 0.05) in cynomolgus monkeys. Correspondingly, volume of distribution at steady-state was also reduced by 5.0 ± 1.1-fold, whereas the half-life change was minimal (1.5-fold), corroborating daprodustat hepatic uptake inhibition by rifampin. A PBPK model accounting for OATP1B-CYP2C8 interplay was developed, which well described daprodustat PK and DDIs with gemfibrozil (CYP2C8 and OATP1B inhibitor) and trimethoprim (weak CYP2C8 inhibitor) within 25% error of the observed data in healthy subjects. About 18-fold increase in daprodustat area under the curve (AUC) following gemfibrozil treatment was found to be associated with strong CYP2C8 inhibition and moderate OATP1B inhibition. Moreover, PK modulation in hepatic dysfunction and subjects with CKD, in comparison to healthy control, was well-captured by the model. CYP2C8 and/or OATP1B inhibitor drugs (e.g., gemfibrozil, clopidogrel, rifampin, and cyclosporine) were predicted to perpetrate moderate-to-strong DDIs in healthy subjects, as well as, in target CKD population. Daprodustat can be used as a sensitive CYP2C8 index substrate in the absence of OATP1B modulation.

Are all measures of liver Kpuu a function of FH, as determined following oral dosing, or have we made a critical error in defining hepatic drug clearance?

Here we present, utilizing universally accepted relationships for hepatic clearance at steady state, that for all models of hepatic elimination the ratio of unbound liver drug concentration to unbound systemic blood concentration, Kpuu, is a function of or related to the hepatic bioavailability for that drug, FH. According to the derivation for the well-stirred model, Kpuu can never exceed unity, can frequently be a function of hepatic blood flow, and is equivalent to the value of FH as determined following oral dosing. For the parallel tube model, Kpuu will not equal FH but will be a function of FH and will also never be a value greater than 1. When hepatic clearance is rate limited by basolateral transporters, Kpuu will be less than 1, and less than FH. We believe that such outcomes are highly unlikely, and that the error arises from a basic assumption concerning hepatic clearance that leads to the mechanistic models of hepatic elimination, the well-stirred, parallel tube and dispersion models. That basic assumption is that the steady-state systemic concentration multiplied by the hepatic systemic clearance is equal to the product of the average unbound liver steady-state concentration and the intrinsic hepatic clearance (Css · CL = CH,u · CLint). Calculations of Kpuu and FH based on present methods of analysis provide a strong argument as to why this universally accepted relationship is not correct. Alternatively, we have shown in recent publications that hepatic clearance may be adequately determined based on Kirchhoff's Laws where no assumption of the above equality concerning hepatic intrinsic clearance is required, and where Kpuu is independent of hepatic extraction ratio and FH.

Uridine 5'-Diphospho-glucuronosyltransferase 1A3 (UGT1A3) Prediction of Hepatic Clearance of Organic Anion Transporting Polypeptide 1B3 (OATP1B3) Substrate Telmisartan by Glucuronidation Using In Vitro-In Vivo Extrapolation (IVIVE)

BACKGROUND AND OBJECTIVE

The prediction of pharmacokinetic parameters for drugs metabolised by cytochrome P450 enzymes has been the subject of active research for many years, while the application of in vitro-in vivo extrapolation (IVIVE) techniques for non-cytochrome P450 enzymes has not been thoroughly evaluated. There is still no established quantitative method for predicting hepatic clearance of drugs metabolised by uridine 5'-diphospho-glucuronosyltransferases (UGTs), not to mention those which undergo hepatic uptake. The objective of the study was to predict the human hepatic clearance for telmisartan based on in vitro metabolic stability and hepatic uptake results.

METHODS

Telmisartan was examined in liver systems, allowing to estimate intrinsic clearance (CLint, in vitro) based on the substrate disappearance rate with the use of liquid chromatography tandem mass spectrometry (LC-MS/MS) technique. Obtained CLint, in vitro values were corrected for corresponding unbound fractions. Prediction of human hepatic clearance was made from scaled unbound CLint, in vitro data with the use of the well-stirred model, and finally referenced to the literature value of observed clearance in humans, allowing determination of the essential scaling factors.

RESULTS

The in vitro scaled CLint, in vitro by UGT1A3 was assessed using three systems, human hepatocytes, liver microsomes, and recombinant enzymes. Obtained values were scaled and hepatic metabolism clearance was predicted, resulting in significant clearance underprediction. Utilization of the extended clearance concept (ECC) and hepatic uptake improved prediction of hepatic metabolism clearance. The scaling factors for hepatocytes, assessing the in vitro-in vivo difference, changed from sixfold difference to only twofold difference with the application of the ECC.

CONCLUSIONS

The study showed that taking into consideration hepatic uptake of a drug allows us to obtain satisfactory scaling factors, hence enabling the prediction of in vivo hepatic glucuronidation from in vitro data.

Improving In Vitro-In Vivo Extrapolation of Clearance Using Rat Liver Microsomes for Highly Plasma Protein-Bound Molecules

It is common practice in drug discovery and development to predict in vivo hepatic clearance from in vitro incubations with liver microsomes or hepatocytes using the well-stirred model (WSM). When applying the WSM to a set of approximately 3000 Novartis research compounds, 73% of neutral and basic compounds (extended clearance classification system [ECCS] class 2) were well-predicted within 3-fold. In contrast, only 44% (ECCS class 1A) or 34% (ECCS class 1B) of acids were predicted within 3-fold. To explore the hypothesis whether the higher degree of plasma protein binding for acids contributes to the in vitro-in vivo correlation (IVIVC) disconnect, 68 proprietary compounds were incubated with rat liver microsomes in the presence and absence of 5% plasma. A minor impact of plasma on clearance IVIVC was found for moderately bound compounds (fraction unbound in plasma [fup] ≥1%). However, addition of plasma significantly improved the IVIVC for highly bound compounds (fup <1%) as indicated by an increase of the average fold error from 0.10 to 0.36. Correlating fup with the scaled unbound intrinsic clearance ratio in the presence or absence of plasma allowed the establishment of an empirical, nonlinear correction equation that depends on fup Taken together, estimation of the metabolic clearance of highly bound compounds was enhanced by the addition of plasma to microsomal incubations. For standard incubations in buffer only, application of an empirical correction provided improved clearance predictions. SIGNIFICANCE STATEMENT: Application of the well-stirred liver model for clearance in vitro-in vivo extrapolation (IVIVE) in rat generally underpredicts the clearance of acids and the strong protein binding of acids is suspected to be one responsible factor. Unbound intrinsic in vitro clearance (CLint,u) determinations using rat liver microsomes supplemented with 5% plasma resulted in an improved IVIVE. An empirical equation was derived that can be applied to correct CLint,u-values in dependance of fraction unbound in plasma (fup) and measured CLint in buffer.

Quantitative prediction of CYP3A-mediated drug-drug interactions by correctly estimating fraction metabolized using human liver chimeric mice

BACKGROUND AND PURPOSE

Fraction metabolized (fm ) and fraction transported (ft ) are important for understanding drug-drug interactions (DDIs) in drug discovery and development. However, current in vitro systems cannot accurately estimate in vivo fm due to inability to reflect the ft by efflux transporters (ft,efflux ). This study demonstrates how CYP3A-mediated DDI for CYP3A/P-gp substrates can be predicted using Hu-PXB mice as human liver chimeric mice.

EXPERIMENTAL APPROACH

For estimating human in vitro fm by CYP3A enzyme (fm,CYP3A,in vitro ), six drugs, including CYP3A/P-gp substrates (alprazolam, cyclosporine, docetaxel, midazolam, prednisolone, and theophylline) and human hepatocytes were incubated with or without ketoconazole as a CYP3A inhibitor. We calculated fm,CYP3A,in vitro based on hepatic intrinsic clearance. To estimate human in vivo fm,CYP3A (fm,CYP3A,in vivo ), we collected information on clinical DDI caused by ketoconazole for these six drugs. We calculated fm,CYP3A,in vivo using the change of total clearance (CLtotal ). For evaluating the human DDI predictability, the six drugs were administered intravenously to Hu-PXB and SCID mice with or without ketoconazole. We calculated the change of CLtotal caused by ketoconazole. We compared the CLtotal change in humans with that in Hu-PXB and SCID mice.

KEY RESULTS

The fm,CYP3A,in vitro was overestimated compared to the fm,CYP3A,in vivo . Hu-PXB mice showed much better correlation in the change of CLtotal with humans (R2  = 0.95) compared to SCID mice (R2  = 0.0058).

CONCLUSIONS AND IMPLICATIONS

CYP3A-mediated DDI can be predicted by correctly estimating human fm,CYP3A,in vivo using Hu-PXB mice. These mice could be useful predicting hepatic fm and ft,efflux .

Generation of a drug-induced renal injury list to facilitate the development of new approach methodologies for nephrotoxicity

Drug-induced renal injury (DIRI) causes >1.5 million adverse events annually in the USA alone. Although standard biomarkers exist for DIRI, they lack the sensitivity or specificity to detect nephrotoxicity before the significant loss of renal function. In this study, we describe the creation of DIRIL - a list of drugs associated with DIRI and nephrotoxicity - from two literature datasets with DIRI annotation, confirmed using FDA drug labeling. DIRIL comprises 317 orally administered drugs covering all 14 anatomical, therapeutic and chemical (ATC) classification categories. Of the 317 drugs, 171 were DIRI-positive and 146 were DIRI-negative. DIRIL will be a relevant and invaluable resource for discovery of new approach methods (NAMs) to predict the occurrence and possible severity of DIRI earlier in drug development.

Next generation risk assessment: an ab initio case study to assess the systemic safety of the cosmetic ingredient, benzyl salicylate, after dermal exposure

We performed an ab initio next-generation risk assessment (NGRA) for a fragrance ingredient, benzyl salicylate (BSal), to demonstrate how cosmetic ingredients can be evaluated for systemic toxicity endpoints based on non-animal approaches. New approach methodologies (NAMs) used to predict the internal exposure included skin absorption assays, hepatocyte metabolism, and physiologically based pharmacokinetic (PBPK) modeling, and potential toxicodynamic effects were assessed using pharmacology profiling, ToxProfiler cell stress assay, transcriptomics in HepG2 and MCF-7 cells, ReproTracker developmental and reproductive toxicology (DART) assays, and cytotoxicity assays in human kidney cells. The outcome of the NGRA was compared to that of the traditional risk assessment approach based on animal data. The identification of the toxicologically critical entity was a critical step that directed the workflow and the selection of chemicals for PBPK modeling and testing in bioassays. The traditional risk assessment and NGRA identified salicylic acid (SA) as the "toxdriver." A deterministic PBPK model for a single-day application of 1.54 g face cream containing 0.5% BSal estimated the Cmax for BSal (1 nM) to be much lower than that of its major in vitro metabolite, SA (93.2 nM). Therefore, SA was tested using toxicodynamics bioassays. The lowest points of departure (PoDs) were obtained from the toxicogenomics assays. The interpretation of these results by two companies and methods were similar (SA only results in significant gene deregulation in HepG2 cells), but PoD differed (213 μM and 10.6 µM). A probabilistic PBPK model for repeated applications of the face cream estimated the highest Cmax of SA to be 630 nM. The resulting margins of internal exposure (MoIE) using the PoDs were 338 and 16, which were more conservative than those derived from external exposure and in vivo PoDs (margin of safety values were 9,705). In conclusion, both traditional and ab initio NGRA approaches concluded that the daily application of BSal in a cosmetic leave-on face cream at 0.5% is safe for humans. The processing and interpretation of toxicogenomics data can lead to different PoDs, which can subsequently affect the calculation of the MoIE. This case study supports the use of NAMs in a tiered NGRA ab initio approach.

Prediction of Human Clearance Using In Silico Models with Reduced Bias

Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.

Pharmaco-toxicological effects of the novel tryptamine hallucinogen 5-MeO-MiPT on motor, sensorimotor, physiological, and cardiorespiratory parameters in mice-from a human poisoning case to the preclinical evidence

RATIONALE

The 5-methoxy-N-methyl-N-isopropyltryptamine (5-MeO-MiPT, known online as "Moxy") is a new psychedelic tryptamine first identified on Italian national territory in 2014. Its hallucinogen effects are broadly well-known; however, only few information is available regarding its pharmaco-toxicological effects.

OBJECTIVES

Following the seizure of this new psychoactive substances by the Arm of Carabinieri and the occurrence of a human intoxication case, in the current study we had the aim to characterize the in vivo acute effects of systemic administration of 5-MeO-MiPT (0.01-30 mg/kg i.p.) on sensorimotor (visual, acoustic, and overall tactile) responses, thermoregulation, and stimulated motor activity (drag and accelerod test) in CD-1 male mice. We also evaluated variation on sensory gating (PPI, prepulse inhibition; 0.01-10 mg/kg i.p.) and on cardiorespiratory parameters (MouseOx and BP-2000; 30 mg/kg i.p.). Lastly, we investigated the in silico ADMET (absorption, distribution, metabolism, excretion, toxicity) profile of 5-MeO-MiPT compared to 5-methoxy-N,N-diisopropyltryptamine (5-MeO-DIPT) and N,N-dimethyltryptamine (DMT).

RESULTS

This study demonstrates that 5-MeO-MiPT dose-dependently inhibits sensorimotor and PPI responses and, at high doses, induces impairment of the stimulated motor activity and cardiorespiratory changes in mice. In silico prediction shows that the 5-MeO-MiPT toxicokinetic profile shares similarities with 5-MeO-DIPT and DMT and highlights a cytochrome risk associated with this compound.

CONCLUSIONS

Consumption of 5-MeO-MiPT can affect the ability to perform activities and pose a risk to human health status, as the correspondence between the effects induced in mice and the symptoms occurred in the intoxication case suggests. However, our findings suggest that 5-MeO-MiPT should not be excluded from research in the psychiatric therapy field.

Novel (±)-trans-β-lactam ureas: Synthesis, in silico and in vitro biological profiling

A diastereomeric mixture of racemic 3-phthalimido-b-lactam 2a/2b was synthesized by the Staudinger reaction of carboxylic acid activated with 2-chloro-1-methylpyridinium iodide and imine 1. The amino group at the C3 position of the b-lactam ring was used for further structural upgrade. trans-b-lactam ureas 4a-t were prepared by the condensation reaction of the amino group of b-lactam ring with various aromatic and aliphatic isocyanates. Antimicrobial activity of compounds 4a-t was evaluated in vitro and neither antibacterial nor antifungal activity were observed. Several of the newly synthesized trans-b-lactam ureas 4a-c, 4f, 4h, 4n, 4o, 4p, and 4s were evaluated for in vitro antiproliferative activity against liver hepatocellular carcinoma (HepG2), ovarian carcinoma (A2780), breast adenocarcinoma (MCF7) and untransformed human fibroblasts (HFF1). The b-lactam urea 4o showed the most potent antiproliferative activity against the ovarian carcinoma (A2780) cell line. Compounds 4o and 4p exhibited strong cytotoxic effects against human non-tumor cell line HFF1. The b-lactam ureas 4a-t were estimated to be soluble and membrane permeable, moderately lipophilic molecules (logP 4.6) with a predisposition to be CYP3A4 and P-glycoprotein substrates. The tools PASS and SwissTargetPrediction could not predict biological targets for compounds 4a-t with high probability, pointing to the novelty of their structure. Considering low toxicity risk, molecules 4a and 4f can be selected as the most promising candidates for further structure modifications.

Discovery of TP0628103: A Highly Potent and Selective MMP-7 Inhibitor with Reduced OATP-Mediated Clearance Designed by Shifting Isoelectric Points

Matrix metalloproteinase-7 (MMP-7) has been shown to play an important role in pathophysiological processes such as cancer and fibrosis. We previously discovered selective MMP-7 inhibitors by molecular hybridization and structure-based drug design. However, the systemic clearance (CLtot) of the biologically active lead compound was very high. Because our studies revealed that hepatic uptake by organic anion transporting polypeptide (OATP) was responsible for the high CLtot, we found a novel approach to reducing their uptake based on isoelectric point (IP) values as an indicator for substrate recognition by OATP1B1/1B3. Our "IP shift strategy" to adjust the IP values culminated in the discovery of TP0628103 (18), which is characterized by reduced in vitro OATP-mediated hepatic uptake and in vivo CLtot. Our in vitro-in vivo extrapolation of OATP-mediated clearance and the "IP shift strategy" provide crucial insights for a new medicinal chemistry approach to reducing the systemic clearance of OATP1B1/1B3 substrates.

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.

Utilization of OATP1B Biomarker Coproporphyrin-I to Guide Drug-Drug Interaction Risk Assessment: Evaluation by the Pharmaceutical Industry

Drug-drug interactions (DDIs) involving hepatic organic anion transporting polypeptides 1B1/1B3 (OATP1B) can be substantial, however, challenges remain for predicting interaction risk. Emerging evidence suggests that endogenous biomarkers, particularly coproporphyrin-I (CP-I), can be used to assess in vivo OATP1B activity. The present work under the International Consortium for Innovation and Quality in Pharmaceutical Development was aimed primarily at assessing CP-I as a biomarker for informing OATP1B DDI risk. Literature and unpublished CP-I data along with pertinent in vitro and clinical DDI information were collected to identify DDIs primarily involving OATP1B inhibition and assess the relationship between OATP1B substrate drug and CP-I exposure changes. Static models to predict changes in exposure of CP-I, as a selective OATP1B substrate, were also evaluated. Significant correlations were observed between CP-I area under the curve ratio (AUCR) or maximum concentration ratio (Cmax R) and AUCR of substrate drugs. In general, the CP-I Cmax R was equal to or greater than the CP-I AUCR. CP-I Cmax R < 1.25 was associated with absence of OATP1B-mediated DDIs (AUCR < 1.25) with no false negative predictions. CP-I Cmax R < 2 was associated with weak OATP1B-mediated DDIs (AUCR < 2). A correlation was identified between CP-I exposure changes and OATP1B1 static DDI predictions. Recommendations for collecting and interpreting CP-I data are discussed, including a decision tree for guiding DDI risk assessment. In conclusion, measurement of CP-I is recommended to inform OATP1B inhibition potential. The current analysis identified changes in CP-I exposure that may be used to prioritize, delay, or replace clinical DDI studies.

Analysis of the interplay of physiological response to food intake and drug properties in food-drug interactions

The effect of food on oral drug absorption is determined by the complex interplay among gut physiological factors and drug properties. The currently used dissolution testing and classification systems (biopharmaceutics classification system, BCS or biopharmaceutics drug disposition classification system, BDDCS) do not account for dynamic changes in gastrointestinal physiology caused by food intake. This study aimed to identify key drug properties that influence food effect (FE) using supervised machine learning approaches. The analysis showed that drugs with high logP, dose number, and extraction ratio have a higher probability of positive FE, while drugs with low permeability and high efflux saturation index have a greater likelihood of negative FE. Weakly acidic drugs also showed a greater probability of positive FE, particularly at pKa >4.3. The importance of drug properties in predicting FE was ranked as logP, dose number, extraction ratio, pKa, and permeability. The accuracy of FE prediction using the models was compared with BCS and extended clearance classification system (ECCS). Overall, the likelihood or magnitude of FE depends on physiological changes to food intake such as altered bile acid secretion rate, intestinal metabolism, transport kinetics, and gastric emptying time, which should be considered along with drug properties (e.g., solubility, logP, and ionization) in predicting FE of orally administered drugs.

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.

Comprehensive evaluation of the pharmacological and toxicological effects of γ-valerolactone as compared to γ-hydroxybutyric acid: Insights from in vivo and in silico models

Γ-valerolactone (GVL), marketed online as "Tranquilli-G" and "excellent Valium", is used as a legal substitute for γ-hydroxybutyric acid (GHB); however, until now, GVL has only been connected to one Drug-Facilitated Sexual Assault (DFSA) case. Moreover, the pharmaco-toxicological effects of GVL are poorly studied. The aim of this study was to investigate the 1) in vivo effects of gavage administration of GVL (100-3000 mg/kg) on neurological (myoclonia, convulsions), sensorimotor (visual, acoustic, and overall tactile) responses, righting reflex, thermoregulation, motor activity (bar, drag, and accelerod test) and cardiorespiratory changes (heart rate, breath rate, oxygen saturation, and pulse distension) in CD-1 male mice and the 2) in silico ADMET profile of GVL in comparison to GHB and the open active form γ-hydroxyvaleric acid (GHV). The present study demonstrates that GVL inhibits, in a dose-dependent manner, sensorimotor and motor responses and induces cardiorespiratory depression (at a dose of 3000 mg/kg) in mice. The determination of the ED50 in sensorimotor and motor responses revealed that GVL is about 4-5 times less potent than GHB. In silico prediction of ADMET profiles revealed toxicokinetic similarities between GHB and GHV, and differences with GVL. These results suggest that GVL could be used as a substitute for GHB and should be added to forensic toxicology screenings.

Exploring the Boundaries for In Vitro-In Vivo Extrapolation: Use of Isolated Rat Hepatocytes in Co-culture and Impact of Albumin Binding Properties in the Prediction of Clearance of Various Drug Types

Prediction of hepatic clearance of drugs (via uptake or metabolism) from in vitro systems continues to be problematic, particularly when plasma protein binding is high. The following work explores simultaneous assessment of both clearance processes, focusing on a commercial hepatocyte-fibroblast co-culture system (HμREL) over a 24-hour period using six probe drugs (ranging in metabolic and transporter clearance and low-to-high plasma protein binding). A rat hepatocyte co-culture assay was established using drug depletion (measuring both medium and total concentrations) and cell uptake kinetic analysis, both in the presence and absence of plasma protein (1% bovine serum albumin). Secretion of endogenous albumin was monitored as a marker of viability, and this reached 0.004% in incubations (at a rate similar to in vivo synthesis). Binding to stromal cells was substantial and required appropriate correction factors. Drug concentration-time courses were analyzed both by conventional methods and a mechanistic cell model prior to in vivo extrapolation. Clearance assayed by drug depletion in conventional suspended rat hepatocytes provided a benchmark to evaluate co-culture value. Addition of albumin appeared to improve predictions for some compounds (where fraction unbound in the medium is less than 0.1); however, for high-binding drugs, albumin significantly limited quantification and thus predictions. Overall, these results highlight ongoing challenges concerning in vitro hepatocyte system complexity and limitations of practical expediency. Considering this, more reliable measurement of hepatically cleared compounds seems possible through judicious use of available hepatocyte systems, including co-culture systems, as described herein; this would include those compounds with low metabolic turnover but high active uptake clearance. SIGNIFICANCE STATEMENT: Co-culture systems offer a more advanced tool than standard hepatocytes, with the ability to be cultured for longer periods of time, yet their potential as an in vitro tool has not been extensively assessed. We evaluate the strengths and limitations of the HμREL system using six drugs representing various metabolic and transporter-mediated clearance pathways with various degrees of albumin binding. Studies in the presence/absence of albumin allow in vitro-in vivo extrapolation and a framework to maximize their utility.

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.

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

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

Design of Next-Generation DGAT2 Inhibitor PF-07202954 with Longer Predicted Half-Life

Diacylglycerol O-acyltransferase 2 (DGAT2) inhibitors have been shown to lower liver triglyceride content and are being explored clinically as a treatment for non-alcoholic steatohepatitis (NASH). This work details efforts to find an extended-half-life DGAT2 inhibitor. A basic moiety was added to a known inhibitor template, and the basicity and lipophilicity were fine-tuned by the addition of electrophilic fluorines. A weakly basic profile was required to find an appropriate balance of potency, clearance, and permeability. This work culminated in the discovery of PF-07202954 (12), a weakly basic DGAT2 inhibitor that has advanced to clinical studies. This molecule displays a higher volume of distribution and longer half-life in preclinical species, in keeping with its physicochemical profile, and lowers liver triglyceride content in a Western-diet-fed rat model.

Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening

Various toxicity and pharmacokinetic evaluations as screening experiments are needed at the drug discovery stage. Currently, to reduce the use of animal experiments and developmental expenses, the development of high-performance predictive models based on quantitative structure-activity relationship analysis is desired. From these evaluation targets, we selected 50% lethal dose (LD50), blood-brain barrier penetration (BBBP), and the clearance (CL) pathway for this investigation and constructed predictive models for each target using 636-11,886 compounds. First, we constructed predictive models using the DeepSnap-deep learning (DL) method and images of compounds as features. The calculated area under the curve (AUC) and balanced accuracy (BAC) were, respectively, 0.887 and 0.818 for LD50, 0.893 and 0.824 for BBBP, and 0.883 and 0.763 for the CL pathway. Next, molecular descriptors (MDs) of compounds were calculated using Molecular Operating Environment, alvaDesc, and ADMET Predictor to construct predictive models using the MD-based method. Using these MDs, we constructed predictive models using DataRobot. The calculated AUC and BAC were, respectively, 0.931 and 0.805 for LD50, 0.919 and 0.849 for BBBP, and 0.900 and 0.807 for the CL pathway. In this investigation, we constructed predictive models combining the DeepSnap-DL and MD-based methods. In ensemble models using the mean predictive probability of the DeepSnap-DL and MD-based methods, the calculated AUC and BAC were, respectively, 0.942 and 0.842 for LD50, 0.936 and 0.853 for BBBP, and 0.908 and 0.832 for the CL pathway, with improved predictive performance observed for all variables compared with either single method alone. Moreover, in consensus models that adopted only compounds for which the results of the two methods agreed, the calculated BAC for LD50, BBBP, and the CL pathway were 0.916, 0.918, and 0.847, respectively, indicating higher predictive performance than the ensemble models for all three variables. The predictive models combining the DeepSnap-DL and MD-based methods displayed high predictive performance for LD50, BBBP, and the CL pathway. Therefore, the application of this approach to prediction targets in various drug discovery screenings is expected to accelerate drug discovery.

A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling

During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.

6-Azaspiro[2.5]octanes as small molecule agonists of the human glucagon-like peptide-1 receptor

Activation of the glucagon-like peptide-1 (GLP-1) receptor stimulates insulin release, lowers plasma glucose levels, delays gastric emptying, increases satiety, suppresses food intake, and affords weight loss in humans. These beneficial attributes have made peptide-based agonists valuable tools for the treatment of type 2 diabetes mellitus and obesity. However, efficient, and consistent delivery of peptide agents generally requires subcutaneous injection, which can reduce patient utilization. Traditional orally absorbed small molecules for this target may offer improved patient compliance as well as the opportunity for co-formulation with other oral therapeutics. Herein, we describe an SAR investigation leading to small-molecule GLP-1 receptor agonists that represent a series that parallels the recently reported clinical candidate danuglipron. In the event, identification of a benzyloxypyrimidine lead, using a sensitized high-throughput GLP-1 agonist assay, was followed by optimization of the SAR using substituent modifications analogous to those discovered in the danuglipron series. A new series of 6-azaspiro[2.5]octane molecules was optimized into potent GLP-1 agonists. Information gleaned from cryogenic electron microscope structures was used to rationalize the SAR of the optimized compounds.

Solubility-Permeability Interplay in Facilitating the Prediction of Drug Disposition Routes, Extent of Absorption, Food Effects, Brain Penetration and Drug Induced Liver Injury Potential

Here I detail the use of measures of permeability rate and solubility in predicting drug disposition characteristics through the utilization of the Biopharmaceutics Drug Disposition Classification System (BDDCS) and the Extended Clearance Classification System (ECCS) as well as the accuracy of the systems in predicting the major route of elimination and the extent of oral absorption of a new small molecule therapeutics. I compare the BDDCS and ECCS with the FDA Biopharmaceutics Classification System (BCS). I also detail the use of the BCS in predicting food effects and the BDDCS in predicting brain disposition of small molecule therapeutics and in validating DILI predictive metrics. This review provides an update of the current status of these classification systems and their uses in the drug development process.

Modelling Based Approaches to Support Generic Drug Regulatory Submissions-Practical Considerations and Case Studies

Model informed drug development (MiDD) is useful to predict in vivo exposure of drugs during various stages of the drug development process. This approach employs a variety of quantitative tools to assess the risks during the drug development process. One important tool in the MiDD tool kit is the Physiologically Based Pharmacokinetic Modelling (PBPK). This tool is extensively used to reduce the development cost and to accelerate the access of medicines to the patients. In this work, we provide an overview of PBPK modelling approaches in the generic drug development process, with a special emphasis on the bio-waiver applications. We describe herein approaches and common pitfalls while submitting model based justifications as a response to the regulatory deficiencies during the generic drug development process. With some in-house case studies, we have attempted to provide a clear path for PBPK model based justifications for bio-waivers. With this review, the gap between theoretical knowledge and practical application of modelling and simulation tools for generic drug product development could be potentially reduced.

Drug Design and Success of Prospective Mouse In Vitro-In Vivo Extrapolation (IVIVE) for Predictions of Plasma Clearance (CLp) from Hepatocyte Intrinsic Clearance (CLint)

Hepatocyte intrinsic clearance (CL_int) and methods of in vitro-in vivo extrapolation (IVIVE) are often used to predict plasma clearance (CL_p) in drug discovery. While the prediction success of this approach is dependent on the chemotype, specific molecular properties and drug design features that govern these outcomes are poorly understood. To address this challenge, we investigated the success of prospective mouse CL_p IVIVE across 2142 chemically diverse compounds. Dilution scaling, which assumes that the free fraction in hepatocyte incubations (f_u,inc) is governed by binding to the 10% of serum in the incubation medium, was used as our default CL_p IVIVE approach. Results show that predictions of CL_p are better for smaller (molecular weight (MW) < 500 Da), less polar (total polar surface area (TPSA) < 100 Ų, hydrogen bond donor (HBD) ≤1, hydrogen bond acceptor (HBA) ≤ 6), lipophilic (log D > 3), and neutral compounds, with low HBD count playing the key role. If compounds are classified according to their chemical space, predictions were good for compounds resembling central nervous system (CNS) drugs [average absolute fold error (AAFE) of 2.05, average fold error (AFE) of 0.90], moderate for classical druglike compounds (according to Lipinski, Veber, and Ghose guidelines; AAFE of 2.55; AFE of 0.68), and poor for nonclassical "beyond the rule of 5" compounds (AAFE of 3.31; AFE of 0.41). From the perspective of measured druglike properties, predictions of CL_p were better for compounds with moderate-to-high hepatocyte CL_int (>10 μL/min/10⁶ cells), high passive cellular permeability (P_app > 100 nm/s), and moderate observed CL_p (5-50 mL/min/kg). Influences of plasma protein binding (f_u,p) and P-glycoprotein (Pgp) apical efflux ratio (AP-ER) were less pronounced. If the extended clearance classification system (ECCS) is applied, predictions were good for class 2 (P_app > 50 nm/s; neutral or basic; AAFE of 2.35; AFE of 0.70) and acceptable for class 1A compounds (AAFE of 2.98; AFE of 0.70). Classes 1B, 3 A/B, and 4 showed poor outcomes (AAFE > 3.80; AFE < 0.60). Functional groups trending toward weaker CL_p IVIVE were esters, carbamates, sulfonamides, carboxylic acids, ketones, primary and secondary amines, primary alcohols, oxetanes, and compounds liable to aldehyde oxidase metabolism, likely due to multifactorial reasons. Multivariate analysis showed that multiple properties are relevant, combining together to define the overall success of CL_p IVIVE. Our results indicate that the current practice of prospective CL_p IVIVE is suitable only for CNS-like compounds and well-behaved classical druglike space (e.g., high permeability or ECCS class 2) without challenging functional groups. Unfortunately, based on existing mouse data, prospective CL_p IVIVE for complex and nonclassical chemotypes is poor and hardly better than random guessing. This is likely due to complexities such as extrahepatic metabolism and transporter-mediated disposition which are poorly captured by this methodology. With small-molecule drug discovery increasingly evolving toward nonclassical and complex chemotypes, existing CL_p IVIVE methodology will require improvement. While empirical correction factors may bridge the gap in the near future, improved and new in vitro assays, data integration models, and machine learning (ML) methods are increasingly needed to address this challenge and reduce the number of nonclinical pharmacokinetic (PK) studies.

In Vitro-In Vivo Extrapolation and Scaling Factors for Clearance of Human and Preclinical Species with Liver Microsomes and Hepatocytes

In vitro-in vivo extrapolation ((IVIVE) and empirical scaling factors (SF) of human intrinsic clearance (CL_int) were developed using one of the largest dataset of 455 compounds with data from human liver microsomes (HLM) and human hepatocytes (HHEP). For extended clearance classification system (ECCS) class 2/4 compounds, linear SFs (SF_lin) are approximately 1, suggesting enzyme activities in HLM and HHEP are similar to those in vivo under physiological conditions. For ECCS class 1A/1B compounds, a unified set of SFs was developed for CL_int. These SFs contain both SF_lin and an exponential SF (SF_β) of fraction unbound in plasma (f_u,p). The unified SFs for class 1A/1B eliminate the need to identify the transporters involved prior to clearance prediction. The underlying mechanisms of these SFs are not entirely clear at this point, but they serve practical purposes to reduce biases and increase prediction accuracy. Similar SFs have also been developed for preclinical species. For HLM-HHEP disconnect (HLM > HHEP) ECCS class 2/4 compounds that are mainly metabolized by cytochrome P450s/FMO, HLM significantly overpredicted in vivo CL_int, while HHEP slightly underpredicted and geometric mean of HLM and HHEP slightly overpredicted in vivo CL_int. This observation is different than in rats, where rat liver microsomal CL_int correlates well with in vivo CL_int for compounds demonstrating permeability-limited metabolism. The good CL_int IVIVE developed using HLM and HHEP helps build confidence for prospective predictions of human clearance and supports the continued utilization of these assays to guide structure-activity relationships to improve metabolic stability.

Plasma Protein-Mediated Uptake and Contradictions to the Free Drug Hypothesis: A Critical Review

According to the free drug hypothesis (FDH), only free, unbound drug is available to interact with biological targets. This hypothesis is the fundamental principle that continues to explain the vast majority of all pharmacokinetic and pharmacodynamic processes. Under the FDH, the free drug concentration at the target site is considered the driver of pharmacodynamic activity and pharmacokinetic processes. However, deviations from the FDH are observed in hepatic uptake and clearance predictions, where observed unbound intrinsic hepatic clearance (CL_int,u) is larger than expected. Such deviations are commonly observed when plasma proteins are present and form the basis of the so-called plasma protein-mediated uptake effect (PMUE). This review will discuss the basis of plasma protein binding as it pertains to hepatic clearance based on the FDH, as well as several hypotheses that may explain the underlying mechanisms of PMUE. Notably, some, but not all, potential mechanisms remained aligned with the FDH. Finally, we will outline possible experimental strategies to elucidate PMUE mechanisms. Understanding the mechanisms of PMUE and its potential contribution to clearance underprediction is vital to improving the drug development process.

Use of In Vivo Imaging and Physiologically-Based Kinetic Modelling to Predict Hepatic Transporter Mediated Drug-Drug Interactions in Rats

Gadoxetate, a magnetic resonance imaging (MRI) contrast agent, is a substrate of organic-anion-transporting polypeptide 1B1 and multidrug resistance-associated protein 2. Six drugs, with varying degrees of transporter inhibition, were used to assess gadoxetate dynamic contrast enhanced MRI biomarkers for transporter inhibition in rats. Prospective prediction of changes in gadoxetate systemic and liver AUC (AUCR), resulting from transporter modulation, were performed by physiologically-based pharmacokinetic (PBPK) modelling. A tracer-kinetic model was used to estimate rate constants for hepatic uptake (khe), and biliary excretion (kbh). The observed median fold-decreases in gadoxetate liver AUC were 3.8- and 1.5-fold for ciclosporin and rifampicin, respectively. Ketoconazole unexpectedly decreased systemic and liver gadoxetate AUCs; the remaining drugs investigated (asunaprevir, bosentan, and pioglitazone) caused marginal changes. Ciclosporin decreased gadoxetate khe and kbh by 3.78 and 0.09 mL/min/mL, while decreases for rifampicin were 7.20 and 0.07 mL/min/mL, respectively. The relative decrease in khe (e.g., 96% for ciclosporin) was similar to PBPK-predicted inhibition of uptake (97-98%). PBPK modelling correctly predicted changes in gadoxetate systemic AUCR, whereas underprediction of decreases in liver AUCs was evident. The current study illustrates the modelling framework and integration of liver imaging data, PBPK, and tracer-kinetic models for prospective quantification of hepatic transporter-mediated DDI in humans.

Building a Human Physiologically Based Pharmacokinetic Model for Aflatoxin B1 to Simulate Interactions with Drugs

Mycotoxins such as aflatoxin B1 (AFB1) are secondary fungal metabolites present in food commodities and part of one’s daily exposure, especially in certain regions, e.g., sub-Saharan Africa. AFB1 is mostly metabolised by cytochrome P450 (CYP) enzymes, namely, CYP1A2 and CYP3A4. As a consequence of chronic exposure, it is interesting to check for interactions with drugs taken concomitantly. A physiologically based pharmacokinetic (PBPK) model was developed based on the literature and in-house-generated in vitro data to characterise the pharmacokinetics (PK) of AFB1. The substrate file was used in different populations (Chinese, North European Caucasian and Black South African), provided by SimCYP® software (v21), to evaluate the impact of populations on AFB1 PK. The model’s performance was verified against published human in vivo PK parameters, with AUC ratios and Cmax ratios being within the 0.5–2.0-fold range. Effects on AFB1 PK were observed with commonly prescribed drugs in South Africa, leading to clearance ratios of 0.54 to 4.13. The simulations revealed that CYP3A4/CYP1A2 inducer/inhibitor drugs might have an impact on AFB1 metabolism, altering exposure to carcinogenic metabolites. AFB1 did not have effects on the PK of drugs at representative exposure concentrations. Therefore, chronic AFB1 exposure is unlikely to impact the PK of drugs taken concomitantly.

Simplifying the Extended Clearance Concept Classification System (EC3S) to Guide Clearance Prediction in Drug Discovery

PURPOSE

The Extended Clearance Concept Classification System was established as a development-stage tool to provide a framework for identifying fundamental mechanism(s) governing drug disposition in humans. In the present study, the applicability of the EC3S in drug discovery has been investigated. In its current format, the EC3S relies on low-throughput hepatocyte uptake data, which are not frequently generated in a discovery setting.

METHODS

A relationship between hepatocyte uptake clearance and MDCK permeability was first established along with intrinsic clearance from human liver microsomes. The performance of this approach was examined by categorizing 64 drugs into EC3S classes and comparing the predicted major elimination pathway(s) to that observed in humans. As an extension of the work, the ability of the simplified EC3S to predict human systemic clearance based on intrinsic clearance generated using in-vitro metabolic systems was evaluated.

RESULTS

The assessment enabled the use of MDCK permeability and unscaled unbound intrinsic clearance to generate cut-off criteria to categorize compounds into four EC3S classes: Class 12ab, 2cd, 34ab, and 34cd, with major elimination mechanism(s) assigned to each class. The predictivity analysis suggested that systemic clearance could generally be predicted within threefold for EC3S class 12ab and 34ab compounds. For classes 2cd and 34cd, systemic clearance was poorly predicted using in-vitro systems explored in this study.

CONCLUSION

Collectively, our simplified classification approach is expected to facilitate the identification of mechanism(s) involved in drug elimination, faster resolution of in-vitro to in-vivo disconnects, and better design of mechanistic pharmacokinetic studies in drug discovery.

Plasma Protein Binding Refinement of the Extended Clearance Classification System: Subclasses for Predicting Hepatic Uptake or Renal Clearance for Classes 1B and 3B

BACKGROUND AND OBJECTIVES

The Extended Clearance Classification System (ECCS) was established to facilitate the timely anticipation of clearance rate determination according to the physicochemical characteristics of a given compound and in vitro passive membrane permeability. Unfortunately, distinguishing between renal and hepatic uptake clearance mechanisms using ECCS class 3B is not possible. We determined the effects of plasma protein binding (PPB) on major hepatic organic anion transporting polypeptide (OATP) and renal organic anion transporter (OAT) substrates. A modified ECCS could predict when renal or hepatic uptake mechanisms were the main clearance rate determinants (accounting for ≥ 70% of total clearance).

METHODS

A dataset of 66 human OATP and 41 OAT substrates was analyzed to determine the effect of PPB. A total of 63 acidic and zwitterionic, and high-molecular-weight (MW > 400 Da) compounds, including 50 drugs in ECCS classes 1B and 3B, were reanalyzed considering their PPB.

RESULTS

Statistical analyses revealed that hepatic uptake transporter (OATP1B1 and OATP1B3) substrates possess a high PPB rate of ≥ 90%, whereas OAT1 and/or OAT3 substrates possess low PPB rates of < 90%. By analyzing the 63 drugs on the basis of their PPB, the active hepatic uptakes of acids and zwitterions were determined to be the main clearance mechanisms, with PPB ≥ 90%, whereas renally eliminated drugs exhibited limited PPB (< 90%).

CONCLUSIONS

Therefore, PPB is an effective parameter for defining clearance rate determination for acidic and zwitterionic drugs with high MWs. Using PPB as an additional parameter in ECCS, clearance mechanisms for class 1B and 3B compounds can be predicted, and OATP and OAT substrates may be readily distinguished.

Effect of Hepatic Impairment on OATP1B Activity: Quantitative Pharmacokinetic Analysis of Endogenous Biomarker and Substrate Drugs

Hepatic impairment (HI) is known to modulate drug disposition and may lead to elevated plasma exposure. The aim of this study was to quantitate the in vivo OATP1B-mediated hepatic uptake activity in populations with varying degrees of HI. First, we measured baseline levels of plasma coproporphyrin-I, an endogenous OATP1B biomarker, in an open-label, parallel cohort study in adult subjects with normal liver function and mild, moderate, and severe HI (n = 24, 6/cohort). The geometric mean plasma concentrations of coproporphyrin-I were 1.66-fold, 2.81-fold (P < 0.05), and 7.78-fold (P < 0.0001) higher in mild, moderate, and severe impairment than those healthy controls. Second, we developed a dataset of 21 OATP1B substrate drugs with HI data extracted from literature. Median disease-to-healthy plasma area under the curve (AUC) ratios for substrate drugs were ~ 1.4, 3.0, and 6.4 for mild, moderate, and severe HI, respectively. Additionally, significant linear relationship was noted between AUC ratios of substrate drugs without and with co-administration of rifampin, a prototypic OATP1B inhibitor, and AUC ratios in moderate (P < 0.01) and severe (P < 0.001) HI. Third, a physiologically-based pharmacokinetic model analysis was conducted with 10 substrate drugs following estimation of relative OATP1B functional activity in virtual disease population models using coproporphyrin-I data and verification of substrate models with rifampin drug-drug interaction data. This approach adequately predicted plasma AUC change particularly in moderate (9 of 10 within 2-fold) and severe (5 of 5 within 2-fold) HI. Collective findings indicate progressive reduction, by as much as 90-92%, in OATP1B activity in the HI population.

Perspective on a chemistry classification system for AI-assisted formulation development

This perspective article draws a distinction between some of the well-known drug classification systems and a "Chemistry Classification System" (CCS). Rather than have drug classification based on some simple properties like solubility and permeability or route of systemic elimination, a CCS results in more than four or five classes and each class has distinct properties that impact formulation development. This perspective provides and outline of 13 classes, but a CCS is a flexible system that introduces a thought process for classification. The number of classes is not rigid, and chemists are encouraged to adapt these methods to their own situations. A CCS utilizes machine-learning models and artificial intelligence (AI) to estimate physicochemical properties that result in unique, frequently observed dissolution, Absorption, Distribution, Metabolism, and Excretion (ADME) properties to guide formulation development.

Discovery of Ervogastat (PF-06865571): A Potent and Selective Inhibitor of Diacylglycerol Acyltransferase 2 for the Treatment of Non-alcoholic Steatohepatitis

Discovery efforts leading to the identification of ervogastat (PF-06865571), a systemically acting diacylglycerol acyltransferase (DGAT2) inhibitor that has advanced into clinical trials for the treatment of non-alcoholic steatohepatitis (NASH) with liver fibrosis, are described herein. Ervogastat is a first-in-class DGAT2 inhibitor that addressed potential development risks of the prototype liver-targeted DGAT2 inhibitor PF-06427878. Key design elements that culminated in the discovery of ervogastat are (1) replacement of the metabolically labile motif with a 3,5-disubstituted pyridine system, which addressed potential safety risks arising from a cytochrome P450-mediated O-dearylation of PF-06427878 to a reactive quinone metabolite precursor, and (2) modifications of the amide group to a 3-THF group, guided by metabolite identification studies coupled with property-based drug design.

Analysis of Time-Dependent Pharmacokinetics Using In Vitro-In Vivo Extrapolation and Physiologically Based Pharmacokinetic Modeling

SCR430, a sorafenib derivative, is an investigational drug exhibiting anti-tumor action. This study aimed to have a mechanistic understanding of SCR430's time-dependent pharmacokinetics (TDPK) through an ex vivo study combined with an in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) modeling. A non-compartmental pharmacokinetic analysis was performed after intravenous SCR430 administration in female Sprague-Dawley rats for a control group (no treatment), a vehicle group (vehicle only, 14 days, PO), and a repeated-dosing group (SCR430, 30 mg/kg/day, 14 days, PO). In addition, hepatic uptake and metabolism modulation were investigated using isolated hepatocytes from each group of rats. The minimal PBPK model based on IVIVE was constructed to explain SCR430's TDPK. Repeated SCR430 administration decreased the systemic exposure by 4.4-fold, which was explained by increased hepatic clearance (4.7-fold). The ex vivo study using isolated hepatocytes from each group suggested that the increased hepatic uptake (9.4-fold), not the metabolic activity, contributes to the increased hepatic clearance. The minimal PBPK modeling based on an ex vivo study could explain the decreased plasma levels after the repeated doses. The current study demonstrates the TDPK after repeated dosing by hepatic uptake induction, not hepatic metabolism, as well as the effectiveness of an ex vivo approach combined with IVIVE and PBPK modeling to investigate the TDPK.

First-in-human study to investigate the safety and pharmacokinetics of salvianolic acid A and pharmacokinetic simulation using a physiologically based pharmacokinetic model

Salvianolic acid A (SAA) is a water-soluble phenolic acid component from Salvia miltiorrhiza Bunge currently under development for myocardial protection treatment for coronary heart disease (CHD). We investigated the safety, tolerability, and pharmacokinetics of single and multiple ascending doses of SAA. Additionally, a physiologically based pharmacokinetic (PBPK) model was developed to simulate the pharmacokinetics of SAA. This was a first-in-human (FIH), randomized, double-blind, placebo-controlled, single, and multiple-dose study in 116 healthy Chinese subjects with the range of 10-300 mg and 60-200 mg SAA, respectively. SAA was well tolerated at all dose levels, following both single and multiple doses, with a low overall incidence of treatment-emergent adverse events (TEAEs) which appeared to be no dose-related. The main pharmacokinetic parameter of SAA, assessed by the power model, was the lack of proportionality with the dose range after single dosing. The 90% CIs of the slope β of Cmax (1.214 [1.150-1.278]) and AUC0-t (1.222 [1.156-1.288]) were not within the predefined acceptance range, and the direction of the deviation was higher than expected. PBPK modeling suggested the transfer ability saturation of hepatic organic anion-transporting polypeptide 1B1 (OATP1B1) and P-glycoprotein (P-gp) might result in a relatively low distribution rate at higher doses. Clinical plasma concentrations observed were in good agreement with PBPK prediction. SAA showed well-characterized pharmacokinetics and was generally well tolerated in the dose range investigated. The PBPK model provides valuable pharmacokinetic knowledge for further clinical development.

Identification of Organic Anion Transporter 2 Inhibitors: Screening, Structure-Based Analysis, and Clinical Drug Interaction Risk Assessment

Organic anion transporter 2 (OAT2 or SLC22A7) plays an important role in the hepatic uptake and renal secretion of several endogenous compounds and drugs. The goal of this work is to understand the structure activity of OAT2 inhibition and assess clinical drug interaction risk. A single-point inhibition assay using OAT2-transfected HEK293 cells was employed to screen about 150 compounds; and concentration-dependent inhibition potency (IC₅₀) was measured for the identified "inhibitors". Acids represented about 65% of all inhibitors, and the frequency of bases-plus-zwitterions approximately doubled for "non-inhibitors". Interestingly, 9 of 10 most potent inhibitors (low IC₅₀) are acids (pK_a ∼ 3-5). Additionally, inhibitors are significantly larger and lipophilic than non-inhibitors. In silico (binary) models were developed to identify inhibitors and non-inhibitors. Finally, in vivo risk assessed via static drug-drug interaction models identified several inhibitors with potential for renal and hepatic OAT2 inhibition at clinical doses. This is the first study assessing the global pattern of OAT2-ligand interactions.

Can We Predict Clinical Pharmacokinetics of Highly Lipophilic Compounds by Integration of Machine Learning or In Vitro Data into Physiologically Based Models? A Feasibility Study Based on 12 Development Compounds

While high lipophilicity tends to improve potency, its effects on pharmacokinetics (PK) are complex and often unfavorable. To predict clinical PK in early drug discovery, we built human physiologically based PK (PBPK) models integrating either (i) machine learning (ML)-predicted properties or (ii) discovery stage in vitro data. Our test set was composed of 12 challenging development compounds with high lipophilicity (mean calculated log P 4.2), low plasma-free fraction (50% of compounds with fu,p < 1%), and low aqueous solubility. Predictions focused on key human PK parameters, including plasma clearance (CL), volume of distribution at steady state (V_ss), and oral bioavailability (%F). For predictions of CL, the ML inputs showed acceptable accuracy and slight underprediction bias [an average absolute fold error (AAFE) of 3.55; an average fold error (AFE) of 0.95]. Surprisingly, use of measured data only slightly improved accuracy but introduced an overprediction bias (AAFE = 3.35; AFE = 2.63). Predictions of V_ss were more successful, with both ML (AAFE = 2.21; AFE = 0.90) and in vitro (AAFE = 2.24; AFE = 1.72) inputs showing good accuracy and moderate bias. The %F was poorly predicted using ML inputs [average absolute prediction error (AAPE) of 45%], and use of measured data for solubility and permeability improved this to 34%. Sensitivity analysis showed that predictions of CL limited the overall accuracy of human PK predictions, partly due to high nonspecific binding of lipophilic compounds, leading to uncertainty of unbound clearance. For accurate predictions of %F, solubility was the key factor. Despite current limitations, this work encourages further development of ML models and integration of their results within PBPK models to enable human PK prediction at the drug design stage, even before compounds are synthesized. Further evaluation of this approach with more diverse chemical types is warranted.

Sinusoidal Organic Anion-Transporting Polypeptide 1B1/1B3 and Bile Canalicular Multidrug Resistance-Associated Protein 2 Play an Essential Role in the Hepatobiliary Disposition of a Synthetic Cyclic Dinucleotide (STING Agonist)

The liver is central to the elimination of many drugs from the body involving multiple processes and understanding of these processes is important to quantitively assess hepatic clearance of drugs. The synthetic STING (STimulator of INterferon Genes protein) agonist is a new class of drugs currently being evaluated in clinical trials as a potential anticancer therapy. In this study, we used ML00960317 (synthetic STING agonist) to investigate the hepatobiliary disposition of this novel molecular entity. A bile-duct cannulated (BDC) rat study indicated that biliary excretion is the major route of elimination for ML00960317 (84% of parent dose in bile). The human biliary clearance using in vitro sandwich cultured human hepatocyte model predicted significant biliary excretion of ML00960317 (biliary excretion index (BEI) of 47%). Moreover, the transport studies using transporter expressing cell lines, hepatocytes, and membrane vesicles indicated that ML00960317 is a robust substrate of OATP1B1, OATP1B3, and MRP2. Using relative expression factor approach, the combined contribution of OATP1B1 (fraction transported (ft) = 0.62) and OATP1B3 (ft = 0.31) was found to be 93% of the active uptake clearance of ML00960317 into the liver. Furthermore, OATP1B1 and OATP1B3-mediated uptake of ML00960317 was inhibited by rifampicin with IC50 of 6.5 and 2.3 μM, respectively indicating an in vivo DDI risk (R value of 1.5 and 2.5 for OATP1B1 and OATP1B3, respectively). These results highlighted an important role of OATP1B1, OATP1B3, and MRP2 in the hepatobiliary disposition of ML00960317. These pathways may act as rate-determining steps in the hepatic clearance of ML00960317 thus presenting clinical DDI risk.

Investigating the uncertainty of prediction accuracy for the application of physiologically based pharmacokinetic models to animal-free risk assessment of cosmetic ingredients

Physiologically based pharmacokinetic (PBPK) models are considered useful tools in animal-free risk assessment. To utilize PBPK models for risk assessment, it is necessary to compare their reliability with in vivo data. However, obtaining in vivo pharmacokinetics data for cosmetic ingredients is difficult, complicating the utilization of PBPK models for risk assessment. In this study, to utilize PBPK models for risk assessment without accuracy evaluation, we proposed a novel concept-the modeling uncertainty factor (MUF). By calculating the prediction accuracy for 150 compounds, we established that using in vitro data for metabolism-related parameters and limiting the applicability domain increase the prediction accuracy of a PBPK model. Based on the 97.5th percentile of prediction accuracy, MUF was defined at 10 for the area under the plasma concentration curve and 6 for C_max. A case study on animal-free risk assessment was conducted for bisphenol A using these MUFs. As this study was conducted mainly on pharmaceuticals, further investigation using cosmetic ingredients is pivotal. However, since internal exposure is essential in realizing animal-free risk assessment, our concept will serve as a useful tool to predict plasma concentrations without using in vivo data.