Predicting in vivo concentrations of dietary hop phytoestrogens by physiologically based kinetic modeling

Hop extracts containing prenylated polyphenols such as 8-prenylnaringenin (8-PN) and its precursor isoxanthohumol (iXN) are popular among women seeking natural alternatives to hormone therapy for postmenopausal symptoms. Due to structural similarities with estrogens, these compounds act as estrogen receptor agonists. Especially 8-PN, described as the most potent phytoestrogen known to date, poses a potential risk for endocrine disruption. Therefore, its use as a hormone replacement raises concerns for human health. However, a significant challenge in assessing the potential endocrine-disruptive effects of hop polyphenols is the lack of data on their toxicokinetics. Particularly, information on in vivo concentrations in target tissues is lacking. To address this gap, we developed a physiologically based kinetic (PBK) model tailored to female physiology. The model was used to predict the levels of hop polyphenols in human blood and target tissues under realistic exposure scenarios. The predictions suggest that iXN and 8-PN concentrations in target tissues reach the low nanomolar range after dietary supplementation. This study enhances our understanding of internal concentrations of iXN and 8-PN after dietary consumption and is of direct applicability for respective risk assessment.

Development of a physiologically-based pharmacokinetic model for Ritonavir characterizing exposure and drug interaction potential at both acute and steady-state conditions

Ritonavir (RTV) is a potent CYP3A inhibitor that is widely used as a pharmacokinetic (PK) enhancer to increase exposure to select protease inhibitors. However, as a strong and complex perpetrator of CYP3A interactions, RTV can also enhance the exposure of other co-administered CYP3A substrates, potentially causing toxicity. Therefore, the prediction of drug-drug interactions (DDIs) and estimation of dosing requirements for concomitantly administered drugs is imperative. In this study, we aimed to develop a physiologically-based PK (PBPK) model for RTV using the PK-sim® software platform. A total of 13 clinical PK studies of RTV covering a wide dose range (100 to 600 mg including both single and multiple dosing), and eight clinical DDI studies with RTV on CYP3A and P-gp substrates, including alprazolam, midazolam, rivaroxaban, clarithromycin, fluconazole, sildenafil, and digoxin were used for the model development and evaluation. Chronopharmacokinetic differences (between morning vs. evening doses) and limitations in parameter estimation for biochemical processes of RTV from in vitro studies were incorporated in the PBPK model. The final developed PBPK model predicted 100% of RTV AUClast and Cmax within a twofold dimension error. The geometric mean fold error (GMFE) from all PK datasets was 1.275 and 1.194, respectively. In addition, 97% of the DDI profiles were predicted with the DDI ratios within a twofold dimension error. The GMFE values from all DDI datasets were 1.297 and 1.212, respectively. Accordingly, this model could be applied to the prediction of DDI profiles of RTV and CYP3A substrates and used to estimate dosing requirements for concomitantly administered drugs.

Absorption, single-dose and steady-state metabolism, excretion, and pharmacokinetics of adagrasib, a KRASG12C inhibitor

OBJECTIVE

This study investigated absorption, metabolism, and excretion of adagrasib after a single oral 600 mg dose (1 µCi [14C]-adagrasib) in 7 healthy subjects and compared the metabolite profile to the profile at steady-state in 4 patients dosed at 600 mg twice daily.

METHODS

Plasma, urine, and feces were collected post [14C]-adagrasib administration and total radioactivity and pooled sample metabolite profiles were determined. Adagrasib pharmacokinetics were determined in plasma and urine. The steady-state plasma metabolite profile was examined in patients and in vitro studies were performed to understand adagrasib's potential to inhibit CYP enzymes and identify CYPs involved in its metabolism.

RESULTS

The total mean recovery of the administered radioactivity was 79.2%, with 74.7% and 4.49% of total radioactivity recovered from feces and urine, respectively. Only 1.8% of the dose was excreted in urine as unchanged adagrasib, indicating negligible renal clearance. Adagrasib, M55a, M11, and M68 were major plasma components accounting for 38.3%, 13.6%, 13.4%, and 11.0% of the total plasma radioactivity exposure, respectively. Metabolite M55a was not detected in plasma at steady state where only M68 (24%) and M11 (17.1%) were abundant. In vitro data showed that CYP3A4 (72%) and CYP2C8 (28%) are main contributors to metabolism and adagrasib is a time-dependent inhibitor of CYP3A4.

CONCLUSION

Elimination of adagrasib is mainly by fecal excretion. Adagrasibs altered metabolite profile at steady state is likely due to CYP3A4 autoinhibition. The abundant steady-state plasma metabolites, M68 and M11, are not human specific and do not contribute significantly to the pharmacological activity of adagrasib.

Non-additivity of the functional properties of individual P450 species and its manifestation in the effects of alcohol consumption on the metabolism of ketamine and amitriptyline

To explore functional interconnections between multiple P450 enzymes and their manifestation in alcohol-induced changes in drug metabolism, we implemented a high-throughput study of correlations between the composition of the P450 pool and the substrate saturation profiles (SSP) of amitriptyline and ketamine demethylation in a series of 23 individual human liver microsomes preparations from donors with a known history of alcohol consumption. The SSPs were approximated with linear combinations of three Michaelis-Menten equations with globally optimized KM (substrate affinity) values. This analysis revealed a strong correlation between the rate of ketamine metabolism and alcohol exposure. For both substrates, alcohol consumption caused a significant increase in the role of the low-affinity enzymes. The amplitudes of the kinetic components and the total rate were further analyzed for correlations with the abundance of 11 major P450 enzymes assessed by global proteomics. The maximal rate of metabolism of both substrates correlated with the abundance of CYP3A4, their predicted principal metabolizer. However, except for CYP2D6 and CYP2E1, responsible for the low-affinity metabolism of ketamine and amitriptyline, respectively, none of the other potent metabolizers of the drugs revealed a positive correlation. Instead, in the case of ketamine, we observed negative correlations with the abundances of CYP1A2, CYP2C9, and CYP3A5. For amitriptyline, the data suggest inhibitory effects of CYP1A2 and CYP2A6. Our results demonstrate the importance of functional interactions between multiple P450 species and their decisive role in the effects of alcohol exposure on drug metabolism.

Characterization of the metabolic contributions of cytochrome P450 isoforms to bicyclol using the relative activity factor method

Bicyclol is a hepatoprotective agent widely used for treating chronic hepatitis and drug-induced liver injuries in clinics. The purpose of the study was to elucidate the contribution of CYP450 enzymes to the metabolism of bicyclol using the relative activity factor approach. After incubation with human liver microsomes and recombinant human liver CYP450 enzymes, the calculated contribution of CYP3A4 and 2C19 to the metabolism of bicyclol was 85.6-90.3% and 9.2-9.7%, respectively. The metabolism was interrupted in the presence of CYP3A4 and 2C19 selective inhibitors. These findings help to predict or avoid metabolic drug-drug interactions or toxicity in clinical applications of bicyclol.

Enzyme-mediated drug-drug interactions: a review of in vivo and in vitro methodologies, regulatory guidance, and translation to the clinic

Enzyme-mediated pharmacokinetic drug-drug interactions can be caused by altered activity of drug metabolizing enzymes in the presence of a perpetrator drug, mostly via inhibition or induction. We identified a gap in the literature for a state-of-the art detailed overview assessing this type of DDI risk in the context of drug development. This manuscript discusses in vitro and in vivo methodologies employed during the drug discovery and development process to predict clinical enzyme-mediated DDIs, including the determination of clearance pathways, metabolic enzyme contribution, and the mechanisms and kinetics of enzyme inhibition and induction. We discuss regulatory guidance and highlight the utility of in silico physiologically-based pharmacokinetic modeling, an approach that continues to gain application and traction in support of regulatory filings. Looking to the future, we consider DDI risk assessment for targeted protein degraders, an emerging small molecule modality, which does not have recommended guidelines for DDI evaluation. Our goal in writing this report was to provide early-career researchers with a comprehensive view of the enzyme-mediated pharmacokinetic DDI landscape to aid their drug development efforts.

The metabolism of the orexin-1 selective receptor antagonist nivasorexant

Nivasorexant was the first orexin-1 selective receptor antagonist entering clinical development. Despite encouraging preclinical evidence in animal models, a proof-of-concept trial in binge-eating patients recently failed to demonstrate its clinical utility in this population.Across species, nivasorexant clearance was driven by metabolism along seven distinct pathways, five of which were hydroxylation reactions in various locations of the molecule. The exact sites of metabolism were identified by means of mass spectrometry, the use of deuterated analogues, and finally confirmed by chemical references.CYP3A4 was the main cytochrome P450 enzyme involved in nivasorexant metabolism in vitro and accounting for about 90% of turnover in liver microsomes. Minor roles were taken by CYP2C9 and CYP2C19 but individually did not exceed 3-7%.In the rat, nivasorexant was mostly excreted via the bile after extensive metabolism, while urinary excretion was negligible. Only traces of the parent drug were detected in urine, bile, or faeces.

A Review of CYP-Mediated Drug Interactions: Mechanisms and In Vitro Drug-Drug Interaction Assessment

Drug metabolism is a major determinant of drug concentrations in the body. Drug-drug interactions (DDIs) caused by the co-administration of multiple drugs can lead to alteration in the exposure of the victim drug, raising safety or effectiveness concerns. Assessment of the DDI potential starts with in vitro experiments to determine kinetic parameters and identify risks associated with the use of comedication that can inform future clinical studies. The diverse range of experimental models and techniques has significantly contributed to the examination of potential DDIs. Cytochrome P450 (CYP) enzymes are responsible for the biotransformation of many drugs on the market, making them frequently implicated in drug metabolism and DDIs. Consequently, there has been a growing focus on the assessment of DDI risk for CYPs. This review article provides mechanistic insights underlying CYP inhibition/induction and an overview of the in vitro assessment of CYP-mediated DDIs.

Coadministration of bedaquiline and pyrifazimine reduce exposure to toxic metabolite N-desmethyl bedaquiline

Background: A new, effective anti-tuberculosis (TB) regimen containing bedaquiline (BDQ) and pyrifazimine (TBI-166) has been recommended for a phase IIb clinical trial. Preclinical drug-drug interaction (DDI) studies of the combination of BDQ and TBI-166 have been designed to support future clinical trials. In this study, we investigated whether a DDI between BDQ and TBI-166 affects the pharmacokinetics of BDQ. Methods: We performed in vitro quantification of the fractional contributions of the fraction of drug metabolism by individual CYP enzymes (f m) of BDQ and the inhibition potency of key metabolic pathways of TBI-166. Furthermore, we conducted an in vivo steady-state pharmacokinetics study in a murine TB model and healthy BALB/c mice. Results: The in vitro f m value indicated that the CYP3A4 pathway contributed more than 75% to BDQ metabolism to N-desmethyl-bedaquiline (M2), and TBI-166 was a moderate (IC50 2.65 µM) potential CYP3A4 inhibitor. Coadministration of BDQ and TBI-166 greatly reduced exposure to metabolite M2 (AUC0-t 76310 vs 115704 h ng/mL, 66% of BDQ alone), whereas the exposure to BDQ and TBI-166 did not changed. The same trend was observed both in healthy and TB model mice. The plasma concentration of M2 decreased significantly after coadministration of BDQ and TBI-166 and decreased further during treatment in the TB model. Conclusions: In conclusion, our results showed that the combination of BDQ and TBI-166 significantly reduced exposure to the toxic metabolite M2 by inhibiting the activity of the CYP3A4 pathway. The potential safety and efficacy benefits demonstrated by the TB treatment highly suggest that coadministration of BDQ and TBI-166 should be studied further.

Development of a PBPK model to quantitatively understand absorption and disposition mechanism and support future clinical trials for PB-201

PB-201 is the second glucokinase activator in the world to enter the phase III clinical trials for the treatment of type 2 diabetes mellitus (T2DM). Combined with the efficacy advantages and the friendly absorption, distribution, metabolism, and excretion characteristics, the indication population of PB-201 will be broad. Because the liver is the primary organ for PB-201 elimination, and the elderly account for 20% of patients with T2DM, it is essential to estimate PB-201 exposure in specific populations to understand the pharmacokinetic characteristics and avoid hypoglycemia. Despite the limited contribution of CYP3A4 to PB-201 metabolism in vivo, the dual effects of nonspecific inhibitors/inducers on PB-201 (substrate for CYP3A4 and CYP2C9 isoenzymes) exposure under fasted and fed states also need to be evaluated to understand potential risks of combination therapy. To grasp the unknown information, the physiologically-based pharmacokinetic (PBPK) model was first developed and the influence of internal and external factors on PB-201 exposure was evaluated. Results are shown that the predictive performance of the mechanistic PBPK model meets the predefined criteria, and can accurately capture the absorption and disposition characteristics. Impaired liver function and age-induced changes in physiological factors may significantly increase the exposure under fasted state by 36%-158% and 48%-82%, respectively. The nonspecific inhibitor (fluconazole) and inducer (rifampicin) may separately increase/decrease PB-201 systemic exposure by 44% and 58% under fasted state, and by 78% and 47% under fed state. Therefore, the influence of internal and external factors on PB-201 exposure deserves attention, and the precision dose can be informed in future clinical studies based on the predicted results.

Physiologically Based Pharmacokinetic Modelling to Predict Pharmacokinetics of Enavogliflozin, a Sodium-Dependent Glucose Transporter 2 Inhibitor, in Humans

Enavogliflozin is a sodium-dependent glucose cotransporter 2 (SGLT2) inhibitor approved for clinical use in South Korea. As SGLT2 inhibitors are a treatment option for patients with diabetes, enavogliflozin is expected to be prescribed in various populations. Physiologically based pharmacokinetic (PBPK) modelling can rationally predict the concentration–time profiles under altered physiological conditions. In previous studies, one of the metabolites (M1) appeared to have a metabolic ratio between 0.20 and 0.25. In this study, PBPK models for enavogliflozin and M1 were developed using published clinical trial data. The PBPK model for enavogliflozin incorporated a non-linear urinary excretion in a mechanistically arranged kidney model and a non-linear formation of M1 in the liver. The PBPK model was evaluated, and the simulated pharmacokinetic characteristics were in a two-fold range from those of the observations. The pharmacokinetic parameters of enavogliflozin were predicted using the PBPK model under pathophysiological conditions. PBPK models for enavogliflozin and M1 were developed and validated, and they seemed useful for logical prediction.

Preclinical development of an EP2 antagonist for post-seizure cognitive deficits

Cognitive comorbidities can substantially reduce quality of life in people with epilepsy. Inflammation is a component of all chronic diseases including epilepsy, as well as acute events like status epilepticus (SE). Neuroinflammation is the consequence of several broad signaling cascades including cyclooxygenase-2 (COX-2)-associated pathways. Activation of the EP2 receptor for prostaglandin E2 appears responsible for blood-brain barrier leakage and much of the inflammatory reaction, neuronal injury and cognitive deficit that follows seizure-provoked COX-2 induction in brain. Here we show that brief exposure of mice to TG11-77, a potent, selective, orally available and brain permeant EP2 antagonist, eliminates the profound cognitive deficit in Y-maze performance after SE and reduces delayed mortality and microgliosis, with a minimum effective i.p. dose (as free base) of 8.8 mg/kg. All in vitro studies required to submit an investigational new drug (IND) application for TG11-77 have been completed, and non-GLP dose range-finding toxicology in the rat identified no overt, organ or histopathology signs of toxicity after 7 days of oral administration at 1000 mg/kg/day. Plasma exposure in the rat was dose-linear between 15 and 1000 mg/kg dosing. TG11-77 thus appears poised to continue development towards the initial clinical test of the hypothesis that EP2 receptor modulation after SE can provide the first preventive treatment for one of the chief comorbidities of epilepsy.

Characterization of the metabolism of eupalinolide A and B by carboxylesterase and cytochrome P450 in human liver microsomes

Eupalinolide A (EA; Z-configuration) and eupalinolide B (EB; E-configuration) are bioactive cis-trans isomers isolated from Eupatorii Lindleyani Herba that exert anti-inflammatory and antitumor effects. Although one pharmacokinetic study found that the metabolic parameters of the isomers were different in rats, metabolic processes relevant to EA and EB remain largely unknown. Our preliminary findings revealed that EA and EB are rapidly hydrolyzed by carboxylesterase. Here, we investigated the metabolic stability and enzyme kinetics of carboxylesterase-mediated hydrolysis and cytochrome P450 (CYP)-mediated oxidation of EA and EB in human liver microsomes (HLMs). We also explored differences in the hydrolytic stability of EA and EB in human liver microsomes and rat liver microsomes (RLMs). Moreover, cytochrome P450 reaction phenotyping of the isomers was performed via in silico methods (i.e., using a quantitative structure-activity relationship model and molecular docking) and confirmed using human recombinant enzymes. The total normalized rate approach was considered to assess the relative contributions of five major cytochrome P450s to EA and EB metabolism. We found that EA and EB were eliminated rapidly, mainly by carboxylesterase-mediated hydrolysis, as compared with cytochrome P450-mediated oxidation. An inter-species difference was observed as well, with faster rates of EA and EB hydrolysis in rat liver microsomes. Furthermore, our findings confirmed EA and EB were metabolized by multiple cytochrome P450s, among which CYP3A4 played a particularly important role.

Development of a Physiologically Based Pharmacokinetic Model for Tegoprazan: Application for the Prediction of Drug-Drug Interactions with CYP3A4 Perpetrators

Tegoprazan is a novel potassium-competitive acid blocker (P-CAB) developed by CJ Healthcare (Korea) for the treatment of gastroesophageal reflux disease and helicobacter pylori infections. Tegoprazan is mainly metabolized by cytochrome P450 (CYP) 3A4. Considering the therapeutic indications, tegoprazan is likely to be administered in combination with various drugs. Therefore, the investigation of drug-drug interactions (DDI) between tegoprazan and CYP3A4 perpetrators is imperative. In the present study, we first aimed to develop a physiologically based pharmacokinetic (PK) model for tegoprazan and its major metabolite, M1, using PK-Sim^®. This model was applied to predict the DDI between tegoprazan and CYP3A4 perpetrators. Clarithromycin, a potent inhibitor of CYP3A4, and rifampicin, a strong inducer of CYP3A4, were selected as case studies. Our results show that clarithromycin significantly increased the exposure of tegoprazan. The area under the concentration-time curve (AUC) and C_max of tegoprazan in the steady state increased up to 4.54- and 2.05-fold, respectively, when tegoprazan (50 mg, twice daily) was coadministered with clarithromycin (500 mg, three times daily). Rifampicin significantly reduced the exposure of tegoprazan. The AUC and C_max of tegoprazan were reduced by 5.71- and 3.51-fold when tegoprazan was coadministered with rifampicin (600 mg, once daily). Due to the high DDI potential, the comedication of tegoprazan with CYP3A4 perpetrators should be controlled. The dosage adjustment for each individual is suggested.

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

Development of a physiologically based pharmacokinetic model to predict irinotecan disposition during inflammation

Irinotecan, a first-line chemotherapy for gastrointestinal (GI) cancers has been causing fatal toxicities like bloody diarrhea and steatohepatitis for years. Irinotecan goes through multiple-step drug metabolism after injection and one of its intermediates 7-ethyl-10-hydroxy-camptothecin (SN-38) is responsible for irinotecan side effect. However, it is unclear what is the disposition kinetics of SN-38 in the organs subjected to toxicity. No studies ever quantified the effect of each enzyme or transporter on SN-38 distribution. In current study, we established a new physiologically based pharmacokinetic (PBPK) model to predict the disposition kinetics of irinotecan. The PBPK model was calibrated with in-house mouse pharmacokinetic data and evaluated with external datasets from the literature. We separated the contribution of each parameters in irinotecan pharmacokinetics by calculating the normalized sensitivity coefficient (NSC). The model gave robust prediction of SN-38 distribution in GI tract, the site of injury. We identified that bile excretion and UDP-glucuronosyltransferases (UGT) played more important roles than fecal excretion and renal clearance in SN-38 pharmacokinetics. Our NSC showed that the impact of enzyme and transporter on irinotecan and SN-38 pharmacokinetics evolved when time continued. Additionally, we mapped out the effect of inflammation on irinotecan metabolic pathways with PBPK modelling. We discovered that inflammation significantly increased the blood and liver exposure of irinotecan and SN-38 in the mice receiving bacterial endotoxin. Inflammation suppressed UGT, microbial metabolism but increased fecal excretion. The present PBPK model can serve as an efficacious and versatile tool to quantitively assess the risk of irinotecan toxicity.

In vitro-in silico-based prediction of inter-individual and inter-ethnic variations in the dose-dependent cardiotoxicity of R- and S-methadone in humans

New approach methodologies predicting human cardiotoxicity are of interest to support or even replace in vivo-based drug safety testing. The present study presents an in vitro–in silico approach to predict the effect of inter-individual and inter-ethnic kinetic variations in the cardiotoxicity of R- and S-methadone in the Caucasian and the Chinese population. In vitro cardiotoxicity data, and metabolic data obtained from two approaches, using either individual human liver microsomes or recombinant cytochrome P450 enzymes (rCYPs), were integrated with physiologically based kinetic (PBK) models and Monte Carlo simulations to predict inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. Chemical specific adjustment factors were defined and used to derive dose–response curves for the sensitive individuals. Our simulations indicated that Chinese are more sensitive towards methadone-induced cardiotoxicity with Margin of Safety values being generally two-fold lower than those for Caucasians for both methadone enantiomers. Individual PBK models using microsomes and PBK models using rCYPs combined with Monte Carlo simulations predicted similar inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. The present study illustrates how inter-individual and inter-ethnic variations in cardiotoxicity can be predicted by combining in vitro toxicity and metabolic data, PBK modelling and Monte Carlo simulations. The novel methodology can be used to enhance cardiac safety evaluations and risk assessment of chemicals.

Integration of Engineered Delivery with the Pharmacokinetics of Medical Candidates via Physiology-Based Pharmacokinetics

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic computational model that can be used to predict a drug product's ADME (absorption, distribution, metabolism, and excretion) and pharmacokinetics (PK). In recent years, PBPK modeling and simulation has been used increasingly to address many biopharmaceutics and clinical pharmacology questions, such as the effect of formulations, intrinsic factors (age, organ dysfunction, etc.), and extrinsic factors (comedications, food) on the PK of an investigational drug product. In this chapter, we will briefly introduce various PBPK models for ADME prediction and general procedures for PBPK modeling and simulations. The readers are encouraged to read updated literature on new applications of PBPK modeling and simulation which is still an emerging area in pharmaceutical development.

Absorption, Metabolism, and Excretion of ACT-1004-1239, a First-In-Class CXCR7 Antagonist: In Vitro, Preclinical, and Clinical Data

ACT-1004-1239 is a potent, selective, first-in-class CXCR7 antagonist, which shows a favorable preclinical and clinical profile. Here we report the metabolites and the metabolic pathways of ACT-1004-1239 identified using results from in vitro and in vivo studies. Two complementary in vitro studies (incubation with human liver microsomes in the absence/presence of cytochrome P450- [CYP] specific chemical inhibitors and incubation with recombinant CYPs) were conducted to identify CYPs involved in ACT-1004-1239 metabolism. For the in vivo investigations, a microtracer approach was integrated in the first-in-human study to assess mass balance and absorption, distribution, metabolism, and excretion (ADME) characteristics of ACT-1004-1239. Six healthy male subjects received orally 100 mg non-radioactive ACT-1004-1239 together with 1 μCi ¹⁴C-ACT-1004-1239. Plasma, urine, and feces samples were collected up to 240 h post-dose and ¹⁴C-drug-related material was measured with accelerator mass spectrometry. This technique was also used to construct radiochromatograms of pooled human samples. Metabolite structure elucidation of human-relevant metabolites was performed using high performance liquid chromatography coupled with high resolution mass spectrometry and facilitated by the use of rat samples. CYP3A4 was identified as the major CYP catalyzing the formation of M1 in vitro. In humans, the cumulative recovery from urine and feces was 84.1% of the dose with the majority being eliminated via the feces (69.6%) and the rest via the urine (14.5%). In human plasma, two major circulating metabolites were identified, i.e., M1 and M23. Elimination via M1 was the only elimination pathway that contributed to ≥25% of ACT-1004-1239 elimination. M1 was identified as a secondary amine metabolite following oxidative N-dealkylation of the parent. M23 was identified as a difluorophenyl isoxazole carboxylic acid metabolite following central amide bond hydrolysis of the parent. Other metabolites observed in humans were A1, A2, and A3. Metabolite A1 was identified as an analog of M1 after oxidative defluorination, whereas both, A2 and A3, were identified as a reduced analog of M1 and parent, respectively, after addition of two hydrogen atoms at the isoxazole ring. In conclusion, CYP3A4 contributes to a relevant extent to ACT-1004-1239 disposition and two major circulating metabolites were observed in humans. Clinical Trial Registration: (https://clinicaltrials.gov/ct2/show/NCT03869320) ClinicalTrials.gov Identifier NCT03869320.

Inter-individual variation in chlorpyrifos toxicokinetics characterized by physiologically based kinetic (PBK) and Monte Carlo simulation comparing human liver microsome and Supersome™ cytochromes P450 (CYP)-specific kinetic data as model input

The present study compares two approaches to evaluate the effects of inter-individual differences in the biotransformation of chlorpyrifos (CPF) on the sensitivity towards in vivo red blood cell (RBC) acetylcholinesterase (AChE) inhibition and to calculate a chemical-specific adjustment factor (CSAF) to account for inter-individual differences in kinetics (HK_AF). These approaches included use of a Supersome^™ cytochromes P450 (CYP)-based and a human liver microsome (HLM)-based physiologically based kinetic (PBK) model, both combined with Monte Carlo simulations. The results revealed that bioactivation of CPF exhibits biphasic kinetics caused by distinct differences in the Km of CYPs involved, which was elucidated by Supersome^™ CYP rather than by HLM. Use of Supersome^™ CYP-derived kinetic data was influenced by the accuracy of the intersystem extrapolation factors (ISEFs) required to scale CYP isoform activity of Supersome^™ to HLMs. The predicted dose–response curves for average, 99th percentile and 1st percentile sensitive individuals were found to be similar in the two approaches when biphasic kinetics was included in the HLM-based approach, resulting in similar benchmark dose lower confidence limits for 10% inhibition (BMDL₁₀) and HK_AF values. The variation in metabolism-related kinetic parameters resulted in HK_AF values at the 99th percentile that were slightly higher than the default uncertainty factor of 3.16. While HK_AF values up to 6.9 were obtained when including also the variability in other influential PBK model parameters. It is concluded that the Supersome^™ CYP-based approach appeared most adequate for identifying inter-individual variation in biotransformation of CPF and its resulting RBC AChE inhibition.

Towards the Elucidation of the Pharmacokinetics of Voriconazole: A Quantitative Characterization of Its Metabolism

The small-molecule drug voriconazole (VRC) shows a complex and not yet fully understood metabolism. Consequently, its in vivo pharmacokinetics are challenging to predict, leading to therapy failures or adverse events. Thus, a quantitative in vitro characterization of the metabolism and inhibition properties of VRC for human CYP enzymes was aimed for. The Michaelis-Menten kinetics of voriconazole N-oxide (NO) formation, the major circulating metabolite, by CYP2C19, CYP2C9 and CYP3A4, was determined in incubations of human recombinant CYP enzymes and liver and intestine microsomes. The contribution of the individual enzymes to NO formation was 63.1% CYP2C19, 13.4% CYP2C9 and 29.5% CYP3A4 as determined by specific CYP inhibition in microsomes and intersystem extrapolation factors. The type of inhibition and inhibitory potential of VRC, NO and hydroxyvoriconazole (OH-VRC), emerging to be formed independently of CYP enzymes, were evaluated by their effects on CYP marker reactions. Time-independent inhibition by VRC, NO and OH-VRC was observed on all three enzymes with NO being the weakest and VRC and OH-VRC being comparably strong inhibitors of CYP2C9 and CYP3A4. CYP2C19 was significantly inhibited by VRC only. Overall, the quantitative in vitro evaluations of the metabolism contributed to the elucidation of the pharmacokinetics of VRC and provided a basis for physiologically-based pharmacokinetic modeling and thus VRC treatment optimization.

INTERSYSTEM EXTRAPOLATION FACTORS (ISEF) ARE SUBSTRATE-DEPENDENT FOR CYP3A4: IMPACT ON CYTOCHROME P450 REACTION PHENOTYPING

The use of intersystem extrapolation factors (ISEF) is required for the quantitative scaling of drug metabolism data generated in individually expressed cytochrome P450 enzymes when estimating fractional contribution to metabolism by P450 enzymes in vivo (f_m,_CYP). For successful prediction of f_m, ISEF values must be universal across all substrates for any individual enzyme. In this study, ISEF values were generated for ten CYP3A4 selective substrates using a common source of recombinant heterologously expressed CYP3A4 and a pool of human liver microsomes. The resulting ISEF values for CYP3A4 were substrate-dependent and ranged 8-fold, with the highest value generated from intrinsic clearance of midazolam depletion (0.36) and the lowest from quinidine depletion (0.044). Application of these ISEF values for estimation of the fractional contribution of CYP3A4 and CYP2C19 to omeprazole clearance yielded values that ranged from 0.21-0.63 and 0.37-0.79, respectively, as compared to back-extrapolated in vivo f_m values of 0.27 (CYP3A4) and 0.85 (CYP2C19) from clinical pharmacokinetic data. For risperidone, estimated f_m values for CYP3A4 and CYP2D6 ranged from 0.87-0.98 and 0.02-0.13, respectively, as compared to in vivo values of 0.36 (CYP3A4) and 0.63-0.88 (CYP2D6), showing that the importance of CYP3A4 was over-estimated and the importance of CYP2D6 under-estimated. Overall, these findings suggest that ISEF values for CYP3A4 can vary with the marker substrate used to derive them, thereby reducing the effectiveness of the approach of using metabolism data from rCYP3A4 with ISEF values for the prediction of f_m values in vivo. Significance Statement Intersystem extrapolation factors (ISEF) are utilized for assigning fractional contributions of individual enzymes to drug clearance (f_m) from drug metabolism data generated in recombinant P450s. The present data shows that ISEF values for cytochrome P4503A4 vary with the substrate. This can lead to variable and erroneous prediction of f_m.

PBPK Modeling as a Tool for Predicting and Understanding Intestinal Metabolism of Uridine 5'-Diphospho-glucuronosyltransferase Substrates

Uridine 5′-diphospho-glucuronosyltransferases (UGTs) are expressed in the small intestines, but prediction of first-pass extraction from the related metabolism is not well studied. This work assesses physiologically based pharmacokinetic (PBPK) modeling as a tool for predicting intestinal metabolism due to UGTs in the human gastrointestinal tract. Available data for intestinal UGT expression levels and in vitro approaches that can be used to predict intestinal metabolism of UGT substrates are reviewed. Human PBPK models for UGT substrates with varying extents of UGT-mediated intestinal metabolism (lorazepam, oxazepam, naloxone, zidovudine, cabotegravir, raltegravir, and dolutegravir) have demonstrated utility for predicting the extent of intestinal metabolism. Drug–drug interactions (DDIs) of UGT1A1 substrates dolutegravir and raltegravir with UGT1A1 inhibitor atazanavir have been simulated, and the role of intestinal metabolism in these clinical DDIs examined. Utility of an in silico tool for predicting substrate specificity for UGTs is discussed. Improved in vitro tools to study metabolism for UGT compounds, such as coculture models for low clearance compounds and better understanding of optimal conditions for in vitro studies, may provide an opportunity for improved in vitro–in vivo extrapolation (IVIVE) and prospective predictions. PBPK modeling shows promise as a useful tool for predicting intestinal metabolism for UGT substrates.

How Science Is Driving Regulatory Guidances

This chapter provides regulatory perspectives on how to translate in vitro drug metabolism findings into in vivo drug-drug interaction (DDI) predictions and how this affects the decision of conducting in vivo DDI evaluation. The chapter delineates rationale and analyses that have supported the recommendations in the U.S. Food and Drug Administration (FDA) DDI guidances in terms of in vitro-in vivo extrapolation of cytochrome P450 (CYP) inhibition-mediated DDI potential for investigational new drugs and their metabolites as substrates or inhibitors. The chapter also describes the framework and considerations to assess UDP-glucuronosyltransferase (UGT) inhibition-mediated DDI potential for drugs as substrates or inhibitors. The limitations of decision criteria and further improvements needed are also discussed. Case examples are provided throughout the chapter to illustrate how decision criteria have been utilized to evaluate in vivo DDI potential from in vitro data.

Predicting human pharmacokinetics from preclinical data: clearance

We have streamlined known in vitro methods used to predict the clearance (CL) of small molecules in humans in this tutorial. There have been many publications on in vitro methods that are used at different steps of human CL prediction. The steps from initial intrinsic CL measurement in vitro to the final application of the well-stirred model to obtain predicted hepatic CL (CL_H) are somewhat complicated. Except for the experts on drug metabolism and PBPK, many drug development scientists found it hard to figure out the entire picture of human CL prediction. To help readers overcome this barrier, we introduce each method briefly and demonstrate its usage in the chain of related equations destined to the CL_H. Despite efforts in the laboratory steps, huge in vitro (predicted CL_H)-in vivo (observed CL_H) discrepancy is not rare. A simple remedy to this discrepancy is to correct human predicted CL_H using the ratio of in vitro-in vivo CL_H obtained from animal species.

Usage of In Vitro Metabolism Data for Drug-Drug Interaction in Physiologically Based Pharmacokinetic Analysis Submissions to the US Food and Drug Administration

The key parameters necessary to predict drug-drug interactions (DDIs) are intrinsic clearance (CL_int ) and fractional contribution of the metabolizing enzyme toward total metabolism (f_m ). Herein, we summarize the accumulated knowledge from 53 approved new drug applications submitted to the Office of Clinical Pharmacology, US Food and Drug Administration, from 2016 to 2018 that contained physiologically based pharmacokinetic (PBPK) models to understand how in vitro data are used in PBPK models to assess drug metabolism and predict DDIs. For evaluation of CL_int and f_m , 29 and 20 new drug applications were included for evaluation, respectively. For CL_int , 86.2% of the PBPK models used modified values based on in vivo data with modifications ranging from -82.5% to 2752.5%. For f_m , 45.0% of the models used modified values with modifications ranging from -28% to 178.6%. When values for CL_int were used from in vitro testing without modification, the model resulted in up to a 14.3-fold overprediction of the area under the concentration-time curve of the substrate. When values for f_m from in vitro testing were used directly, the model resulted in up to a 2.9-fold underprediction of its DDI magnitude with an inducer, and up to a 1.7-fold overprediction of its DDI magnitude with an inhibitor. Our analyses suggested that the in vitro system usually provides a reasonable estimation of f_m when the drug metabolism by a given CYP pathway is more than 70% of the total clearance. In vitro experiments provide important information about basic PK properties of new drugs and can serve as a starting point for building a PBPK model. However, key PBPK parameters such as CL_int and f_m still need to be optimized based on in vivo data.

Human Fetal Liver Metabolism of Oxycodone Is Mediated by CYP3A7

Oxycodone is an opioid analgesic that is commonly prescribed to pregnant women to treat moderate-to-severe pain. It has been shown to cross the placenta and distribute to the fetus. Oxycodone is mainly metabolized by CYP3A4 in the adult liver. Since CYP3A7 is abundantly expressed in the fetal liver and has overlapping substrate specificity with CYP3A4, we hypothesized that the fetal liver may significantly limit fetal exposure to oxycodone. This study showed that oxycodone is metabolized by CYP3A7 to noroxycodone in fetal liver microsomes (FLMs). The measured CYP3A7 expression was 191-409 pmol/mg protein in 14 FLMs, and an intersystem extrapolation factor (ISEF) for CYP3A7 was 0.016-0.066 in the panel of fetal livers using 6β-OH-testosterone formation as the probe reaction. Noroxycodone formation in the fetal liver was predicted from formation rate by recombinant CYP3A7, CYP3A7 expression level and the established ISEF value with average fold error of 1.25. Based on the intrinsic clearance of oxycodone measured in FLM, the fetal hepatic clearance (CLh) at term was predicted to be 495 (range: 66.4-936) μL/min, a value that is > 99% lower than the predicted adult liver CLh. The predicted fetal hepatic extraction ratio was 0.0019 (range: 0.00003-0.0036). These results suggest that fetal liver metabolism does not quantitatively contribute to the total systemic clearance of oxycodone in pregnant women nor does it provide a barrier for limiting fetal exposure to oxycodone. Additionally, since CYP3A7 forms noroxycodone, an inactive metabolite, the metabolism in the fetal liver is unlikely to affect fetal opioid activity.

Nonadditivity in human microsomal drug metabolism revealed in a study with coumarin 152, a polyspecific cytochrome P450 substrate

We closely characterized 7-Dimethylamino-4-trifluromethylcoumarin (Coumarin 152, C152), a substrate metabolized by multiple P450 species, to establish a new fluorogenic probe for the studies of functional integration in the cytochrome P450 ensemble. Scanning fluorescence spectroscopy and LC/MS-MS were used to characterize the products of N-demethylation of C152 and optimize their fluorometric detection. The metabolism of C152 by the individual P450 species was characterized using the microsomes containing cDNA-expressed enzymes. C152 metabolism in human liver microsomes (HLM) was studied in a preparation with quantified content of eleven P450 species. C152 is metabolized by CYP2B6, CYP3A4, CYP3A5, CYP2C19, CYP1A2, CYP2C9, and CYP2C8 listed in the order of decreasing turnover. The affinities exhibited by CYP3A5, CYP2C9, and CYP2C8 were lower than those characteristic to the other enzymes. The presumption of additivity suggests the participation of CYP3A4, CYP2B6, and CYP2C19 to be 84, 8, and 0.2%, respectively. Contrary to this prediction, inhibitory analysis identified CYP2C19 as the principal C152-metabolizing enzyme. We thoroughly characterize C152 for the studies of drug metabolism in HLM and demonstrate the limitations of the proportional projection approach by providing an example, where the involvement of individual P450 species cannot be predicted from their content.

Diagnosing intake and rationalizing toxicities associated with 5F-MDMB-PINACA and 4F-MDMB-BINACA abuse

5F-MDMB-PINACA and 4F-MDMB-BINACA are synthetic cannabinoids (SCs) that elicit cannabinoid psychoactive effects. Defining pharmacokinetic-pharmacodynamic (PK-PD) relationships governing SCs and their metabolites are paramount to investigating their in vivo toxicological outcomes. However, the disposition kinetics and cannabinoid receptor (CB) activities of the primary metabolites of SCs are largely unknown. Additionally, reasons underlying the selection of ester hydrolysis metabolites (EHMs) as urinary biomarkers are often unclear. Here, metabolic reaction phenotyping was performed to identify key metabolizing enzymes of the parent SCs. Hepatic clearances of parent SCs and their EHMs were estimated from microsomal metabolic stability studies. Renal clearances were simulated using a mechanistic kidney model incorporating in vitro permeability and organic anionic transporter 3 (OAT3)-mediated uptake data. Overall clearances were considered in tandem with estimated volumes of distribution for in vivo biological half-lives (t_1/2) predictions. Interactions of the compounds with CB1 and CB2 were investigated using a G-protein coupled receptor activation assay. We demonstrated that similar enzymatic isoforms were implicated in the metabolism of 5F-MDMB-PINACA and 4F-MDMB-BINACA. Our in vivo t_1/2 determinations verified the rapid elimination of parent SCs and suggest prolonged circulation of their EHMs. The pronounced attenuation of the potencies and efficacies of the metabolites against CB1 and CB2 further suggests how toxic manifestations of SC abuse are likely precipitated by augmented exposure to parent SCs. Notably, basolateral OAT3-mediated uptake of the EHMs substantiates their higher urinary abundance. These novel insights underscore the importance of mechanistic, quantitative and systematic characterization of PK-PD relationships in rationalizing the toxicities of SCs.

Dose Optimization in Kidney Disease: Opportunities for PBPK Modeling and Simulation

Kidney disease affects pharmacokinetic (PK) profiles of not only renally cleared drugs but also nonrenally cleared drugs. The impact of kidney disease on drug disposition has not been fully elucidated, but describing the extent of such impact is essential for conducting dose optimization in kidney disease. Accurate evaluation of kidney function has been a clinical interest for dose optimization, and more scientists pay attention and conduct research for clarifying the role of drug transporters, metabolic enzymes, and their interplay in drug disposition as kidney disease progresses. Physiologically based pharmacokinetic (PBPK) modeling and simulation can provide valuable insights for dose optimization in kidney disease. It is a powerful tool to integrate discrete knowledge from preclinical and clinical research and mechanistically investigate system- and drug-dependent factors that may contribute to the changes in PK profiles. PBPK-based prediction of drug exposures may be used a priori to adjust dosing regimens and thereby minimize the likelihood of drug-related toxicity. With real-time clinical studies, parameter estimation may be performed with PBPK approaches that can facilitate identification of sources of interindividual variability. PBPK modeling may also facilitate biomarker research that aids dose optimization in kidney disease. U.S. Food and Drug Administration guidances related to conduction of PK studies in kidney impairment and PBPK documentation provide the foundation for facilitating model-based dose-finding research in kidney disease.

Physiologically Based Pharmacokinetic/Pharmacodynamic Modeling to Predict the Impact of CYP2C9 Genetic Polymorphisms, Co-Medication and Formulation on the Pharmacokinetics and Pharmacodynamics of Flurbiprofen

Physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) models can serve as a powerful framework for predicting the influence as well as the interaction of formulation, genetic polymorphism and co-medication on the pharmacokinetics and pharmacodynamics of drug substances. In this study, flurbiprofen, a potent non-steroid anti-inflammatory drug, was chosen as a model drug. Flurbiprofen has absolute bioavailability of ~95% and linear pharmacokinetics in the dose range of 50–300 mg. Its absorption is considered variable and complex, often associated with double peak phenomena, and its pharmacokinetics are characterized by high inter-subject variability, mainly due to its metabolism by the polymorphic CYP2C9 (fmCYP2C9 ≥ 0.71). In this study, by leveraging in vitro, in silico and in vivo data, an integrated PBPK/PD model with mechanistic absorption was developed and evaluated against clinical data from PK, PD, drug-drug and gene-drug interaction studies. The PBPK model successfully predicted (within 2-fold) 36 out of 38 observed concentration-time profiles of flurbiprofen as well as the CYP2C9 genetic effects after administration of different intravenous and oral dosage forms over a dose range of 40–300 mg in both Caucasian and Chinese healthy volunteers. All model predictions for C_max, AUC_inf and CL/F were within two-fold of their respective mean or geometric mean values, while 90% of the predictions of C_max, 81% of the predictions of AUC_inf and 74% of the predictions of Cl/F were within 1.25 fold. In addition, the drug-drug and drug-gene interactions were predicted within 1.5-fold of the observed interaction ratios (AUC, C_max ratios). The validated PBPK model was further expanded by linking it to an inhibitory E_max model describing the analgesic efficacy of flurbiprofen and applying it to explore the effect of formulation and genetic polymorphisms on the onset and duration of pain relief. This comprehensive PBPK/PD analysis, along with a detailed translational biopharmaceutic framework including appropriately designed biorelevant in vitro experiments and in vitro-in vivo extrapolation, provided mechanistic insight on the impact of formulation and genetic variations, two major determinants of the population variability, on the PK/PD of flurbiprofen. Clinically relevant specifications and potential dose adjustments were also proposed. Overall, the present work highlights the value of a translational PBPK/PD approach, tailored to target populations and genotypes, as an approach towards achieving personalized medicine.

The Ontogeny of UDP-glucuronosyltransferase Enzymes, Recommendations for Future Profiling Studies and Application Through Physiologically Based Pharmacokinetic Modelling

Limited understanding of drug pharmacokinetics in children is one of the major challenges in paediatric drug development. This is most critical in neonates and infants owing to rapid changes in physiological functions, especially in the activity of drug-metabolising enzymes. Paediatric physiologically based pharmacokinetic models that integrate ontogeny functions for cytochrome P450 enzymes have aided our understanding of drug exposure in children, including those under the age of 2 years. Paediatric physiologically based pharmacokinetic models have consequently been recognised by the European Medicines Agency and the US Food and Drug Administration as innovative tools in paediatric drug development and regulatory decision making. However, little is currently known about age-related changes in UDP-glucuronosyltransferase-mediated metabolism, which represents the most important conjugation reaction for xenobiotics. Therefore, the objective of the review was to conduct a thorough literature survey to summarise our current understanding of age-related changes in UDP-glucuronosyltransferases as well as associated clinical and experimental sources of variance. Our findings indicate that there are distinct differences in UDP-glucuronosyltransferase expression and activity between isoforms for different age groups. In addition, there is substantial variability between individuals and laboratories reported for human liver microsomes, which results in part from a lack of standardised experimental conditions. Therefore, we provide a number of best practice recommendations for experimental conditions, which ultimately may help improve the quality of data used for quantitative clinical pharmacology approaches, and thus for safe and effective pharmacotherapy in children.

In vitro-in vivo extrapolation of metabolic clearance using human liver microsomes: factors showing variability and their normalization

In vitro-in vivo extrapolation (IVIVE) using human liver microsomes has been widely used to predict metabolic clearance, but some of the factors used in the process of prediction show variability for the same compound: notably, microsomal intrinsic clearance values corrected by the unbound fraction (CL_{int, u}), physiological parameters used for scale-up, and the source of in vivo clearance data.The purpose of this study was to assess the correlation between in vitro and in vivo CL_int with a focus on factors showing variability using four cytochrome P450 (CYP)3A substrates.We surveyed in vivo clearance values in literature and also determined the microsomal CL_{int, u} values. A scaling factor (SF_direct) was defined as in vivo CL_int divided by the microsomal CL_{int, u}, which ranged from 1190 to 2310 (mg protein per kg body weight). The application of a mean SF_direct of 1600 (mg protein per kg body weight) and further normalization by the microsomal CL_{int, u} values of midazolam, the most commonly used substrate, resulted in improved prediction accuracy for CL_{int, u} values from various microsomal batches.The results suggest the normalization of variability might be useful for predicting the in vivo CL_int.

Implementation of a Physiologically Based Pharmacokinetic Modeling Approach to Guide Optimal Dosing Regimens for Imatinib and Potential Drug Interactions in Paediatrics

Long-term use of imatinib is effective and well-tolerated in children with chronic myeloid leukaemia (CML) yet defining an optimal dosing regimen for imatinib in younger patients is a challenge. The potential interactions between imatinib and coadministered drugs in this "special" population also remains largely unexplored. This study implements a physiologically based pharmacokinetic (PBPK) modeling approach to investigate optimal dosing regimens and potential drug interactions with imatinib in the paediatric population. A PBPK model for imatinib was developed in the Simcyp Simulator (version 17) utilizing in silico, in vitro drug metabolism, and in vivo pharmacokinetic data and verified using an independent set of published clinical pharmacokinetic data. The model was then extrapolated to children and adolescents (aged 2-18 years) by incorporating developmental changes in organ size and maturation of drug-metabolising enzymes and plasma protein responsible for imatinib disposition. The PBPK model described imatinib pharmacokinetics in adult and paediatric populations and predicted drug interaction with carbamazepine, a cytochrome P450 (CYP)3A4 and 2C8 inducer, with a good accuracy (evaluated by visual inspections of the simulation results and predicted pharmacokinetic parameters that were within 1.25-fold of the clinically observed values). The PBPK simulation suggests that the optimal dosing regimen range for imatinib is 230-340 mg/m2/d in paediatrics, which is supported by the recommended initial dose for treatment of childhood CML. The simulations also highlighted that children and adults being treated with imatinib have similar vulnerability to CYP modulations. A PBPK model for imatinib was successfully developed with an excellent performance in predicting imatinib pharmacokinetics across age groups. This PBPK model is beneficial to guide optimal dosing regimens for imatinib and predict drug interactions with CYP modulators in the paediatric population.

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

Extrapolation of pharmacokinetic (PK) parameters from in vitro or in vivo animal to human is one of the main tasks in the drug development process. Translational approaches provide evidence for go or no-go decision-making during drug discovery and the development process, and the prediction of human PKs prior to the first-in-human clinical trials. In vitro-in vivo extrapolation and allometric scaling are the choice of method for projection to human situations. Although these methods are useful tools for the estimation of PK parameters, it is a challenge to apply these methods since underlying biochemical, mathematical, physiological, and background knowledge of PKs are required. In addition, it is difficult to select an appropriate methodology depending on the data available. Therefore, this review covers the principles of PK parameters pertaining to the clearance, volume of distribution, elimination half-life, absorption rate constant, and prediction method from the original idea to recently developed models in order to introduce optimal models for the prediction of PK parameters.

Human hepatocytes and cytochrome P450-selective inhibitors predict variability in human drug exposure more accurately than human recombinant P450s

BACKGROUND AND PURPOSE

Drugs metabolically eliminated by several enzymes are less vulnerable to variable compound exposure in patients due to drug-drug interactions (DDI) or if a polymorphic enzyme is involved in their elimination. Therefore, it is vital in drug discovery to accurately and efficiently estimate and optimize the metabolic elimination profile.

EXPERIMENTAL APPROACH

CYP3A and/or CYP2D6 substrates with well described variability in vivo in humans due to CYP3A DDI and CYP2D6 polymorphism were selected for assessment of fraction metabolized by each enzyme (fmCYP ) in two in vitro systems: (i) human recombinant P450s (hrP450s) and (ii) human hepatocytes combined with selective P450 inhibitors. Increases in compound exposure in poor versus extensive CYP2D6 metabolizers and by the strong CYP3A inhibitor ketoconazole were mathematically modelled and predicted changes in exposure were compared with in vivo data.

KEY RESULTS

Predicted changes in exposure were within twofold of reported in vivo values using fmCYP estimated in human hepatocytes and there was a strong linear correlation between predicted and observed changes in exposure (r2  = 0.83 for CYP3A, r2  = 0.82 for CYP2D6). Predictions using fmCYP in hrP450s were not as accurate (r2  = 0.55 for CYP3A, r2  = 0.20 for CYP2D6).

CONCLUSIONS AND IMPLICATIONS

The results suggest that variability in human drug exposure due to DDI and enzyme polymorphism can be accurately predicted using fmCYP from human hepatocytes and CYP-selective inhibitors. This approach can be efficiently applied in drug discovery to aid optimization of candidate drugs with a favourable metabolic elimination profile and limited variability in patients.

Strategies for Determining Correct Cytochrome P450 Contributions in Hepatic Clearance Predictions: In Vitro-In Vivo Extrapolation as Modelling Approach and Tramadol as Proof-of Concept Compound

BACKGROUND AND OBJECTIVE

Although the measurement of cytochrome P450 (CYP) contributions in metabolism assays is straightforward, determination of actual in vivo contributions might be challenging. How representative are in vitro for in vivo CYP contributions? This article proposes an improved strategy for the determination of in vivo CYP enzyme-specific metabolic contributions, based on in vitro data, using an in vitro-in vivo extrapolation (IVIVE) approach. Approaches are exemplified using tramadol as model compound, and CYP2D6 and CYP3A4 as involved enzymes.

METHODS

Metabolism data for tramadol and for the probe substrates midazolam (CYP3A4) and dextromethorphan (CYP2D6) were gathered in human liver microsomes (HLM) and recombinant human enzyme systems (rhCYP). From these probe substrates, an activity-adjustment factor (AAF) was calculated per CYP enzyme, for the determination of correct hepatic clearance contributions. As a reference, tramadol CYP contributions were scaled-back from in vivo data (retrograde approach) and were compared with the ones derived in vitro. In this view, the AAF is an enzyme-specific factor, calculated from reference probe activity measurements in vitro and in vivo, that allows appropriate scaling of a test drug's in vitro activity to the 'healthy volunteer' population level. Calculation of an AAF, thus accounts for any 'experimental' or 'batch-specific' activity difference between in vitro HLM and in vivo derived activity.

RESULTS

In this specific HLM batch, for CYP3A4 and CYP2D6, an AAF of 0.91 and 1.97 was calculated, respectively. This implies that, in this batch, the in vitro CYP3A4 activity is 1.10-fold higher and the CYP2D6 activity 1.97-fold lower, compared to in vivo derived CYP activities.

CONCLUSION

This study shows that, in cases where the HLM pool does not represent the typical mean population CYP activities, AAF correction of in vitro metabolism data, optimizes CYP contributions in the prediction of hepatic clearance. Therefore, in vitro parameters for any test compound, obtained in a particular batch, should be corrected with the AAF for the respective enzymes. In the current study, especially the CYP2D6 contribution was found, to better reflect the average in vivo situation. It is recommended that this novel approach is further evaluated using a broader range of compounds.

Biosynthesis and Identification of Metabolites of Maraviroc and Their Use in Experiments to Delineate the Relative Contributions of Cytochrome P4503A4 versus 3A5

Maraviroc (MVC) is a CCR5 coreceptor antagonist indicated in combination with other antiretroviral agents for the treatment of CCR5-tropic human immunodefinciency virus-1 infection. In this study, the metabolism of MVC was investigated in human liver microsomes to delineate the relative roles of CYP3A4 and CYP3A5. MVC is metabolized to five hydroxylated metabolites, all of which were biosynthesized and identified using mass and NMR spectroscopy. The sites of metabolism were the 2- and 3-positions of the 4,4-difluorocyclohexyl moiety and the methyl of the triazole moiety. Absolute configurations were ultimately ascertained by comparison to authentic standards. The biosynthesized metabolites were used for quantitative in vitro experiments in liver microsomes using cyp3cide, a selective inactivator of CYP3A4. (1S,2S)-2-OH-MVC was the main metabolite representing approximately half of the total metabolism, and CYP3A5 contributed approximately 40% to that pathway in microsomes from CYP3A5*1/*1 donors. The other four metabolites were almost exclusively metabolized by CYP3A4. (1S,2S)-2-hydroxylation also correlated to T-5 N-oxidation, a CYP3A5-specific activity. These data are consistent with clinical pharmacokinetic data wherein CYP3A5 extensive metabolizer subjects showed a modestly lower exposure to MVC.

Predicted contributions of cytochrome P450s to drug metabolism in human liver microsomes using relative activity factor were dependent on probes

Contributions of cytochrome P450 (CYP450) isoforms to drug metabolism are often predicted using relative activity factor (RAF) method, assuming RAF values were independent of probe. We aimed to report probe-dependent characteristic of RAF values using CYP3A4 or CYP2C9 probes. Metabolism of four CYP3A4 probes (testosterone, midazolam, verapamil and atorvastatin) and three CYP2C9 probes (tolbutamide, diclofenac and S-warfarin) in human liver microsomes (HLM) and cDNA-expressed recombinant CYP450 (Rec-CYP450) systems were characterized and RAF_CL value was estimated as ratio of probe intrinsic clearance in HLM to that in Rec-CYP450. CYP450i contributions to metabolic reaction of a probe were predicted using other probes and compared with data from specific inhibitions. Contributions of CYP3A4 and CYP2C9 to metabolism of deoxypodophyllotoxin and nateglinide were also predicted. RAF values were dependent on probes, leading to probe-dependently predicted contributions. Predicted contributions of CYP3A4 to formations of 6β-hydroxytestosterone, 1'-hydroxymidazolam, norverapamil, ortho-hydroxyatorvastatin and para-hydroxyatorvastatin using other probes were 47.46-219.46%, 21.62-98.87%, 186.49-462.44%, 21.87-101.15% and 53.62-247.97%, respectively. Predicted contributions of CYP3A4 and CYP2C9 to nateglinide metabolism were 8.18-37.84% and 36.08-94.04%, separately. In conclusion, CYP450i contribution to drug metabolism in HLM estimated using RAF approach were probe-dependent. Therefore, contribution of each isoform must be confirmed by multiple probes.

Estimation of fractions metabolized by hepatic CYP enzymes using a concept of inter-system extrapolation factors (ISEFs) - a comparison with the chemical inhibition method

BACKGROUND

For estimation of fractions metabolized (fm) by different hepatic recombinant human CYP enzymes (rhCYP), calculation of inter-system extrapolation factors (ISEFs) has been proposed.

METHODS

ISEF values for CYP1A2, CYP2C19 and CYP3A4/5 were measured. A CYP2C9 ISEF was taken from a previous report. Using a set of compounds, fractions metabolized by CYP enzymes (fm,CYP) values calculated with the ISEFs based on rhCYP data were compared with those from the chemical inhibition data. Oral pharmacokinetics (PK) profiles of midazolam were simulated using the physiologically based pharmacokinetics (PBPK) model with the CYP3A ISEF. For other CYPs, the in vitro fm,CYP values were compared with the reference fm,CYP data back-calculated with, e.g. modeling of test substrates by feeding clinical PK data.

RESULTS

In vitro-in vitro fm,CYP3A4 relationship between the results from rhCYP incubation and chemical inhibition was drawn as an exponential correlation with R2=0.974. A midazolam PBPK model with the CYP3A4/5 ISEFs simulated the PK profiles within twofold error compared to the clinical observations. In a limited number of cases, the in vitro methods could not show good performance in predicting fm,CYP1A2, fm,CYP2C9 and fm,CYP2C19 values as reference data.

CONCLUSIONS

The rhCYP data with the measured ISEFs provided reasonable calculation of fm,CYP3A4 values, showing slight over-estimation compared to chemical inhibition.

Quantitative in vitro phenotyping and prediction of drug interaction potential of CYP2B6 substrates as victims

1. Determination of f_{m, CYP} for a compound is critical to assess the potential risk of a drug candidate as a victim of DDI. Several compounds are identified as CYP2B6 substrates, but the f_{m, CYP2B6} values are not determined quantitatively. 2. Two methods of reaction phenotyping, the chemical inhibition method and metabolism in rCYP enzymes, were used to determine the relative contributions of the enzymes. Chemical inhibition method was also conducted in the presence of BSA (0.5% w/v). 3. The results confirm with the earlier studies concerning the identity of the CYP2B6 enzyme. The f_{m, CYP2B6} values for artemisinin, bupropion, clopidogrel, ketamine, selegiline, sertraline and ticlopidine were 0.24, 0.28, 0.15, 0.45, 0.46, 0.42 and 0.54, respectively, in HLM determined by chemical inhibition method. The f_{m, CYP2B6} values for artemisinin, bupropion, clopidogrel, ketamine, selegiline, sertraline and ticlopidine were 0.46, 0.17, 0.15, 0.60, 0.51, 0.66 and 0.77, respectively, in HLM determined by chemical inhibition method in the presence of BSA (0.5% w/v). 4. Bupropion metabolism is majorly mediated by CYP2C19 (0.41) with a minor contribution from CYP2B6 (0.16) in the presence of BSA. Ticlopidine is a time-dependent inhibitor of both CYP2B6 and CYP2C19 that can inhibit the bupropion metabolism by 50-60%.

Database of Optimized Proteomic Quantitative Methods for Human Drug Disposition-Related Proteins for Applications in Physiologically Based Pharmacokinetic Modeling

The purpose of this study was to create an open access repository of validated liquid chromatography tandem mass spectrometry (LC‐MS/MS) multiple reaction monitoring (MRM) methods for quantifying 284 important proteins associated with drug absorption, distribution, metabolism, and excretion (ADME). Various in silico and experimental approaches were used to select surrogate peptides and optimize instrument parameters for LC‐MS/MS quantification of the selected proteins. The final methods were uploaded to an online public database (QPrOmics; www.qpromics.uw.edu/qpromics/assay/), which provides essential information for facile method development in triple quadrupole mass spectrometry (MS) instruments. To validate the utility of the methods, the differential tissue expression of 107 key ADME proteins was characterized in the tryptic digests of the pooled subcellular fractions of human liver, kidneys, intestines, and lungs. These methods and the data are critical for development of physiologically based pharmacokinetic (PBPK) models to predict xenobiotic disposition.

Assessment of drug metabolism enzyme and transporter pharmacogenetics in drug discovery and early development: perspectives of the I-PWG

Genetic variants of drug metabolism enzymes and transporters can result in high pharmacokinetic and pharmacodynamic variability, unwanted characteristics of efficacious and safe drugs. Ideally, the contributions of these enzymes and transporters to drug disposition can be predicted from in vitro experiments and in silico modeling in discovery or early development, and then be utilized during clinical development. Recently, regulatory agencies have provided guidance on the preclinical investigation of pharmacogenetics, for application to clinical drug development. This white paper summarizes the results of an industry survey conducted by the Industry Pharmacogenomics Working Group on current practice and challenges with using in vitro systems and in silico models to understand pharmacogenetic causes of variability in drug disposition.