Predicting human pharmacokinetics from preclinical data: clearance

Cannabidiol metabolism in vitro: the role of antiseizure medications and CYP2C19 genotypes

1. Cannabidiol (CBD) can be used as add-on antiseizure medication. We aimed to investigate CBD depletion kinetics when combined with antiseizure medications, further the effect of intermediate-activity CYP2C19 genotype on CBD metabolism.

2. CBD depletion in pooled human liver microsomes (HLMs) was studied at varying concentrations (400-6000 nM) in the absence and presence of valproic acid, clobazam, stiripentol and topiramate. In addition, CBD metabolism in HLMs from CYP2C19 *1/*2 or *1/*4 donors was investigated. Incubation samples were analysed for CBD and metabolites 7-OH-CBD and 7-COOH-CBD using LC-MS/MS. CBD in vitro intrinsic clearance (CLint) was calculated using estimated Vmax and Km values and further extrapolated to in vivo CLint.

3. In vitro CLint values were reduced by approximately 25-85% in the presence of antiseizure medication(s) with the largest effect observed for the combination of four antiseizure drugs. There was no discernible difference for HLMs with CYP2C19 *1/*2 or *1/*4 genotype. Increases in CBD depletion half-lives at higher concentrations indicated substrate inhibition and/or metabolic saturation.

4. Projected decreases in CBD CLint values when combined with several antiseizure medications suggest a potential for clinically relevant drug-drug interactions. A 1.3- to 4.8-fold increased exposure to unbound systemic CBD concentrations was predicted when combined with these antiseizure medications.

Formulation Strategy of BCS-II Drugs by Coupling Mechanistic In-Vitro and Nonclinical In-Vivo Data with PBPK: Fundamentals of Absorption-Dissolution to Parameterization of Modelling and Simulation

BCS class II candidates pose challenges in drug development due to their low solubility and permeability. Researchers have explored various techniques; co-amorphous and solid dispersion are major approaches to enhance in-vitro drug solubility and dissolution. However, in-vivo oral bioavailability remains challenging. Physiologically based pharmacokinetic (PBPK) modeling with a detailed understanding of drug absorption, distribution, metabolism, and excretion (ADME) using a mechanistic approach is emerging. This review summarizes the fundamentals of the PBPK, dissolution-absorption models, parameterization of oral absorption for BCS class II drugs, and provides information about newly emerging artificial intelligence/machine learning (AI/ML) linked PBPK approaches with their advantages, disadvantages, challenges and areas of further exploration. Additionally, the fully integrated workflow for formulation design for investigational new drugs (INDs) and virtual bioequivalence for generic molecules falling under BCS-II are discussed.

Phase I Study to Evaluate the Effect of Hepatic Impairment on Pharmacokinetics and Safety of Tegoprazan, a Potassium Competitive Acid Blocker

INTRODUCTION

Tegoprazan is a potassium-competitive acid blocker, and its systemic exposure is presumably affected by hepatic clearance and bioavailability. This study aimed to investigate the effect of hepatic impairment (HI) on the safety and pharmacokinetics of tegoprazan and metabolite.

METHODS

An open-label, multicenter, parallel-group study was conducted in patients with mild (n = 8), moderate (n = 8) and severe (n = 1) HI according to the Child-Pugh classification as well as controls. Healthy subjects (n = 8) were matched to patients with the moderate category based on age, body mass index and sex. Blood and urine samples were obtained to evaluate the concentrations of tegoprazan and metabolite (M1) until 48 h after a single oral administration of 50 mg of tegoprazan.

RESULTS

The geometric mean ratio with a 90% confidence interval of maximum plasma concentration and area under the plasma concentration-time curve for tegoprazan in patients with impaired hepatic function compared to controls were 0.8228 (0.4997-1.3550) and 1.2264 (0.7447-2.0197) in the mild category, 1.0332 (0.6274-1.7015) and 1.7676 (1.0733-2.9109) in the moderate category, and 1.0699 (0.3713-3.0823) and 1.9567 (0.6792-5.6377) in the severe category. The half-life, apparent clearance, renal clearance, and fraction unbound to plasma protein were comparable across study groups. The plasma concentration of M1 increased and decreased faster in the normal group. Tegoprazan was generally well tolerated in patients with HI.

CONCLUSIONS

Systemic exposure to tegoprazan tended to be increased in subjects with HI. The difference between patients with mild HI and the controls was deemed not to require dose adjustment for tegoprazan.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov identifiers: NCT04494269 (31 Jul 2020).

Beyond CL and VSS: A comprehensive approach to human pharmacokinetic predictions

This article presents a comprehensive examination of processes related to the prediction of human pharmacokinetics (PK), a crucial task of clinical drug candidate selection. By systematically incorporating in vitro absorption, distribution, metabolism and excretion (ADME) and in vivo PK data with expert judgement, the study achieves high-quality human PK predictions for 40 orally administered compounds from Boehringer Ingelheim's new chemical entity (NCE) portfolio. Overall, the article provides a detailed evaluation of and guidance for a structured process to predict full concentration-time profiles beyond single-parameter predictions, using state-of-the-art methodologies. Furthermore, it discusses future challenges and improvements, and aims to provide valuable insights for scientists working in drug metabolism and PK (DMPK) or PK/pharmacodynamics (PK/PD) modelling.

Physiologically Based Pharmacokinetic Modeling to Unravel the Drug-gene Interactions of Venlafaxine: Based on Activity Score-dependent Metabolism by CYP2D6 and CYP2C19 Polymorphisms

BACKGROUND

Venlafaxine (VEN) is a commonly utilized medication for alleviating depression and anxiety disorders. The presence of genetic polymorphisms gives rise to considerable variations in plasma concentrations across different phenotypes. This divergence in phenotypic responses leads to notable differences in both the efficacy and tolerance of the drug.

PURPOSE

A physiologically based pharmacokinetic (PBPK) model for VEN and its metabolite O-desmethylvenlafaxine (ODV) to predict the impact of CYP2D6 and CYP2C19 gene polymorphisms on VEN pharmacokinetics (PK).

METHODS

The parent-metabolite PBPK models for VEN and ODV were developed using PK-Sim® and MoBi®. Leveraging prior research, derived and implemented CYP2D6 and CYP2C19 activity score (AS)-dependent metabolism to simulate exposure in the drug-gene interactions (DGIs) scenarios. The model's performance was evaluated by comparing predicted and observed values of plasma concentration-time (PCT) curves and PK parameters values.

RESULTS

In the base models, 91.1%, 94.8%, and 94.6% of the predicted plasma concentrations for VEN, ODV, and VEN + ODV, respectively, fell within a twofold error range of the corresponding observed concentrations. For DGI scenarios, these values were 81.4% and 85% for VEN and ODV, respectively. Comparing CYP2D6 AS = 2 (normal metabolizers, NM) populations to AS = 0 (poor metabolizers, PM), 0.25, 0.5, 0.75, 1.0 (intermediate metabolizers, IM), 1.25, 1.5 (NM), and 3.0 (ultrarapid metabolizers, UM) populations in CYP2C19 AS = 2.0 group, the predicted DGI AUC0-96 h ratios for VEN were 3.65, 3.09, 2.60, 2.18, 1.84, 1.56, 1.34, 0.61, and for ODV, they were 0.17, 0.35, 0.51, 0.64, 0.75, 0.83, 0.90, 1.11, and the results were similar in other CYP2C19 groups. It should be noted that PK differences in CYP2C19 phenotypes were not similar across different CYP2D6 groups.

CONCLUSIONS

In clinical practice, the impact of genotyping on the in vivo disposition process of VEN should be considered to ensure the safety and efficacy of treatment.

Rat Pharmacokinetics and In Vitro Metabolite Identification of KM-819, a Parkinson's Disease Candidate, Using LC-MS/MS and LC-HRMS

FAF1 (FAS-associated factor 1) is involved in the activation of Fas cell surface death receptors and plays a role in apoptosis and necrosis. In patients with Parkinson's disease, FAF1 is overexpressed in dopaminergic neurons in the substantia nigra. KM-819, an FAF1 inhibitor, has shown potential for preventing dopaminergic neuronal cell death, promoting the degradation of α-synuclein and preventing its accumulation. This study aimed to develop and validate a quantitative analytical method for determining KM-819 levels in rat plasma using liquid chromatography-tandem mass spectrometry. This method was then applied to pharmacokinetic (PK) studies in rats. The metabolic stability of KM-819 was assessed in rat, dog, and human hepatocytes. In vitro metabolite identification and metabolic pathways were investigated in rat, dog, and human hepatocytes. The structural analog of KM-819, namely N-[1-(4-bromobenzyl)-3,5-dimethyl-1H-pyrazol-4-yl]-2-(phenylsulfanyl) acetamide, served as the internal standard (IS). Proteins were precipitated from plasma samples using acetonitrile. Analysis was carried out using a reverse-phase C18 column with a mobile phase consisting of 0.1% formic acid in distilled water and 0.1% formic acid in acetonitrile. The analytical method developed for KM-819 exhibited linearity within the concentration range of 0.002-10 μg/mL in rat plasma. The precision and accuracy of the intra- and inter-day measurements were <15% for the lower limit of quantification (LLOQ) and all quality control samples. KM-819 demonstrated stability under various sample storage conditions (6 h at room temperature (25 °C), four weeks at -20 °C, three freeze-thaw cycles, and pretreated samples in the autosampler). The matrix effect and dilution integrity met the criteria set by the Food and Drug Administration and the European Medicines Agency. This sensitive, rapid, and reliable analytical method was successfully applied in pharmacokinetic studies in rats. Pharmacokinetic analysis revealed the dose-independent kinetics of KM-819 at 0.5-5 mg/kg, with a moderate oral bioavailability of ~20% in rats. The metabolic stability of KM-819 was also found to be moderate in rat, dog, and human hepatocytes. Metabolite identification in rat, dog, and human hepatocytes resulted in the discovery of six, six, and eight metabolites, respectively. Glucuronidation and mono-oxidation have been proposed as the major metabolic pathways. Overall, these findings contribute to a better understanding of the pharmacokinetic characteristics of KM-819, thereby aiding future clinical studies.

Predicting Maternal and Infant Tetrahydrocannabinol Exposure in Lactating Cannabis Users: A Physiologically Based Pharmacokinetic Modeling Approach

A knowledge gap exists in infant tetrahydrocannabinol (THC) data to guide breastfeeding recommendations for mothers who use cannabis. In the present study, a paired lactation and infant physiologically based pharmacokinetic (PBPK) model was developed and verified. The verified model was used to simulate one hundred virtual lactating mothers (mean age: 28 years, body weight: 78 kg) who smoked 0.32 g of cannabis containing 14.14% THC, either once or multiple times. The simulated breastfeeding conditions included one-hour post smoking and subsequently every three hours. The mean peak concentration (Cmax) and area under the concentration-time curve (AUC(0-24 h)) for breastmilk were higher than in plasma (Cmax: 155 vs. 69.9 ng/mL; AUC(0-24 h): 924.9 vs. 273.4 ng·hr/mL) with a milk-to-plasma AUC ratio of 3.3. The predicted relative infant dose ranged from 0.34% to 0.88% for infants consuming THC-containing breastmilk between birth and 12 months. However, the mother-to-infant plasma AUC(0-24 h) ratio increased up to three-fold (3.4-3.6) with increased maternal cannabis smoking up to six times. Our study demonstrated the successful development and application of a lactation and infant PBPK model for exploring THC exposure in infants, and the results can potentially inform breastfeeding recommendations.

Evaluating the impact of co-administered drug and disease on ripretinib exposure: A physiologically-based pharmacokinetic modeling approach

Ripretinib, as an oral kinase inhibitor, has been approved to treat advanced gastrointestinal stromal tumors (GIST) and is often used in combination with other drugs to slow disease progression, thus potential drug-drug Interactions (DDIs) and drug-disease interactions (DDZIs) have received much attention. To guide clinical rational drug use, this study assessed the effect of co-administered drugs and diseases on ripretinib exposure. Simcyp® Simulator was used to develop the physiologically-based pharmacokinetic (PBPK) model of ripretinib, which was validated and refined with clinical data. We then examined the impact of several CYP3A4 inhibitors and inducers as well as different diseases on ripretinib exposure using the validated model. In the DDI simulation, moderate CYP3A4 inhibitors and inducers changed the exposure of ripretinib by 1.25-2 fold. In hepatic impairment (HI), the simulation showed that ripretinib's AUC increased by 32%, 100%, and 152% for Child-Pugh A, B, and C classification while C_max increased by 2%, 10%, and 15%, respectively. In renal impairment (RI), the model-simulated AUC in moderate and severe RIs increased by 27% and 20%. In conclusion, PBPK models demonstrated quantitative prediction of ripretinib's pharmacokinetic changes under varying conditions that might be useful for its rational use.

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

BACKGROUND AND OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Development and Biological Characterization of a Novel Selective TrkA Agonist with Neuroprotective Properties against Amyloid Toxicity

Neurotrophins are growth factors that exert important neuroprotective effects by preventing neuronal death and synaptic loss. Nerve Growth Factor (NGF) acts through the activation of its high-affinity, pro-survival TrkA and low-affinity, pro-apoptotic p75NTR receptors. NGF has been shown to slow or prevent neurodegenerative signals in Alzheimer's Disease (AD) progression. However, its low bioavailability and its blood-brain-barrier impermeability limit the use of NGF as a potential therapeutic agent against AD. Based on our previous findings on synthetic dehydroepiandrosterone derivatives, we identified a novel NGF mimetic, named ENT-A013, which selectively activates TrkA and exerts neuroprotective, anti-amyloid-β actions. We now report the chemical synthesis, in silico modelling, metabolic stability, CYP-mediated reaction phenotyping and biological characterization of ENT-A013 under physiological and neurodegenerative conditions. We show that ENT-A013 selectively activates the TrkA receptor and its downstream kinases Akt and Erk1/2 in PC12 cells, protecting these cells from serum deprivation-induced cell death. Moreover, ENT-A013 promotes survival of primary Dorsal Root Ganglion (DRG) neurons upon NGF withdrawal and protects hippocampal neurons against Amyloid β-induced apoptosis and synaptic loss. Furthermore, this neurotrophin mimetic partially restores LTP impairment. In conclusion, ENT-A013 represents a promising new lead molecule for developing therapeutics against neurodegenerative disorders, such as Alzheimer's Disease, selectively targeting TrkA-mediated pro-survival signals.

Prediction of metabolizing enzyme-mediated clinical drug interactions using in vitro information

Evaluation of drug interactions is an essential step in the new drug development process. Regulatory agencies, including U.S. Food and Drug Administrations and European Medicines Agency, have been published documents containing guidelines to evaluate potential drug interactions. Here, we have streamlined in vitro experiments to assess metabolizing enzyme-mediated drug interactions and provided an overview of the overall process to evaluate potential clinical drug interactions using in vitro data. An experimental approach is presented when an investigational drug (ID) is either a victim or a perpetrator, respectively, and the general procedure to obtain in vitro drug interaction parameters is also described. With the in vitro inhibitory and/or inductive parameters of the ID, basic, static, and/or dynamic models were used to evaluate potential clinical drug interactions. In addition to basic and static models which assume the most conservative conditions, such as the concentration of perpetrators as C_max, dynamic models including physiologically-based pharmacokinetic models take into account changes in in vivo concentrations and metabolizing enzyme levels over time.