Prediction of Drug-Drug Interactions for Highly Plasma Protein Bound Compounds

Accurate prediction of drug-drug interactions (DDI) from in vitro data is important, as it provides insights on clinical DDI risk and study design. Historically, the lower limit of plasma fraction unbound (fu,p) is set at 1% for DDI prediction of highly bound compounds by the regulatory agencies due to the uncertainty of the fu,p measurements. This leads to high false positive DDI predictions for highly bound compounds. The recently published ICH M12 DDI guideline allows the use of experimental fu,p for DDI prediction of highly bound compounds. To further build confidence in DDI prediction of highly bound compounds using experimental fu,p values, we evaluated a set of drugs with fu,p < 1% and clinical DDI > 20% using both basic and mechanistic static models. All the compounds evaluated were flagged for DDI risk with the mechanistic model using experimental fu,p values. There was no false negative DDI prediction. Similarly, using the basic model, the DDI risk of all the compounds was identified except for CYP2D6 inhibition of almorexant. The totality of the data demonstrates that the DDI potential of highly bound compounds can be predicted accurately when actual protein binding numbers are measured.

In vitro assessment of inhibitory effects of kinase inhibitors on CYP2C9, 3A and 1A2: Prediction of drug-drug interaction risk with warfarin and direct oral anticoagulants

OBJECTIVE

This study aimed to evaluate the cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) potential of kinase inhibitors with warfarin and direct oral anticoagulants (DOACs).

METHODS

An in vitro CYP probe substrate cocktail assay was used to study the inhibitory effects of fifteen kinase inhibitors on CYP2C9, 3A, and 1A2. Then, DDI predictions were performed using both mechanistic static and physiologically-based pharmacokinetic (PBPK) models.

RESULTS

Linsitinib, masitinib, regorafenib, tozasertib, trametinib, and vatalanib were identified as competitive CYP2C9 inhibitors (Ki = 1.4, 1.0, 1.1, 3.8, 0.5, and 0.1 μM, respectively). Masitinib and vatalanib were competitive CYP3A inhibitors (Ki = 1.3 and 0.2 μM), and vatalanib noncompetitively inhibited CYP1A2 (Ki = 2.0 μM). Moreover, linsitinib and tozasertib were CYP3A time-dependent inhibitors (KI = 26.5 and 400.3 μM, kinact = 0.060 and 0.026 min-1, respectively). Only linsitinib showed time-dependent inhibition of CYP1A2 (KI = 13.9 μM, kinact = 0.018 min-1). Mechanistic static models identified possible DDI risks for linsitinib and vatalanib with (S)-/(R)-warfarin, and for masitinib with (S)-warfarin. PBPK simulations further confirmed that vatalanib may increase (S)- and (R)-warfarin exposure by 4.37- and 1.80-fold, respectively, and that linsitinib may increase (R)-warfarin exposure by 3.10-fold. Mechanistic static models predicted a smaller risk of DDIs between kinase inhibitors and apixaban or rivaroxaban. The greatest AUC increases (1.50-1.74) were predicted for erlotinib in combination with apixaban and rivaroxaban. Linsitinib, masitinib, and vatalanib were predicted to have a smaller effect on apixaban and rivaroxaban AUCs (AUCR 1.22-1.53). No kinase inhibitor was predicted to increase edoxaban exposure.

CONCLUSIONS

Our results suggest that several kinase inhibitors, including vatalanib and linsitinib, can cause CYP-mediated drug-drug interactions with warfarin and, to a lesser extent, with apixaban and rivaroxaban. The work provides mechanistic insights into the risk of DDIs between kinase inhibitors and anticoagulants, which can be used to avoid preventable DDIs in the clinic.

Quantitative Prediction of Drug-Drug Interactions Caused by CYP3A Induction Using Endogenous Biomarker 4β-Hydroxycholesterol

Evaluation of the CYP3A induction risk is important in early drug development stages. This study focused on 4β-hydroxycholesterol (4β-HC) as an endogenous biomarker of drug-drug interactions (DDIs) caused by CYP3A induction. We investigated a new approach using 4β-HC for quantitative prediction of DDIs caused by CYP3A induction based on the mechanistic static pharmacokinetic (MSPK) model. The induction ratio, i.e., the ratio of plasma 4β-HC or 4β-HC/cholesterol (4β-HC/C) with and without a coadministered CYP3A inducer, and the ratio of the area under the plasma concentration-time curve (AUCR), i.e., the ratio of the AUC of plasma CYP3A substrate drugs with and without a coadministered CYP3A inducer, were collected. The scaling factor (d) in the MSPK model was calculated from the induction ratio of 4β-HC or 4β-HC/C based on the systemic term in the MSPK model. The AUCR of 18 CYP3A substrates with and without coadministration of seven CYP3A inducers were then predicted by substituting the calculated d value into the MSPK model. This approach showed that approximately 84% of the predicted AUCR values were within a twofold range of the observed values, showing that this approach can be a good tool to quantitatively predict DDIs caused by CYP3A induction. SIGNIFICANCE STATEMENT: A concise approach to predict drug interactions with adequate accuracy is preferable in the early drug development stage. In this study, a new approach using 4β-hydroxycholesterol for quantitative prediction of drug-drug interactions caused by CYP3A induction was investigated. The predictability was verified using seven CYP3A inducers and 18 substrates.

Cytochrome P450-mediated metabolic interactions between donepezil and tadalafil in human liver microsomes

Donepezil and tadalafil, commonly prescribed among older persons to treat dementia and erectile dysfunction, respectively, are primarily metabolized by cytochrome P450 (CYP) 3A4. However, the drug-drug interactions (DDIs) of these drugs are unknown. Therefore, this study evaluated the CYP-mediated metabolic interaction between donepezil and tadalafil using pooled human liver microsomes (HLMs) to predict their DDI potential. Donepezil metabolism was tadalafil-concentration dependently changed in HLMs incubated with 0.1 μM donepezil and showed the maximum 32.3% increase in the donepezil half-life at 1 μM tadalafil. The formation rates of donepezil metabolites, such as N-desbenzyl donepezil and 3-hydroxy donepezil, decreased by 28.3% and 30.3%, respectively, in HLMs incubated with 1 μM tadalafil and 0.1 μM donepezil. In contrast, neither the half-life of tadalafil nor the production rate of its metabolite, desmethylene tadalafil, was changed by >20% in the presence of donepezil (up to 1 μM). CYP3A4 activity was inhibited by tadalafil with an IC50 value of 22.6 μM but not by donepezil. After pre-incubating HLMs with tadalafil and NADPH, the tadalafil IC50 value against CYP3A4 was approximately 7.04-fold lower, suggesting time-dependent tadalafil inhibition. This study shows that the DDI between donepezil and tadalafil is primarily due to time-dependent inhibition against CYP3A4 by tadalafil.

The Drug-Drug Interaction between Erlotinib and OSI-930 Is Mediated through Aldehyde Oxidase Inhibition

The propensity for aldehyde oxidase (AO) substrates to be implicated in drug-drug interactions (DDIs) is not well understood due to the dearth of potent inhibitors that elicit in vivo inhibition of AO. Although there is only one reported instance of DDI that has been ascribed to the inhibition of AO to date, the supporting evidence for this clinical interaction is rather tenuous, and its veracity has been called into question. Our group recently reported that the epidermal growth factor receptor inhibitor erlotinib engendered potent time-dependent inhibition of AO with inactivation kinetic constants in the same order of magnitude as its free circulating plasma concentrations. At the same time, it was previously reported that the concomitant administration of erlotinib with the investigational drug OSI-930 culminated in a an approximately twofold increase in its systemic exposure. Although the basis underpinning this interaction remains unclear, the structure of OSI-930 contains a quinoline motif that is amenable to oxidation at the electrophilic carbon adjacent to the nitrogen atom by molybdenum-containing hydroxylases like AO. In this study, we conducted metabolite identification that revealed that OSI-930 undergoes AO metabolism to a mono-oxygenated 2-oxo metabolite and assessed its formation kinetics in human liver cytosol. Additionally, reaction phenotyping in human hepatocytes revealed that AO contributes nearly 50% to the overall metabolism of OSI-930. Finally, modeling the interaction between erlotinib and OSI-930 using a mechanistic static model projected an ∼1.85-fold increase in the systemic exposure of OSI-930, which accurately recapitulated clinical observations. SIGNIFICANCE STATEMENT: This study delineates an aldehyde oxidase (AO) metabolic pathway in the investigational drug OSI-930 for the first time and confirmed that it represented a major route of metabolism through reaction phenotyping in human hepatocytes. Our study provided compelling mechanistic and modeling evidence for the first instance of an AO-mediated clinical drug-drug interaction stemming from the in vivo inhibition of the AO-mediated quinoline 2-oxidation pathway in OSI-930 by erlotinib.

Quantitative prediction of CYP3A induction-mediated drug-drug interactions in clinical practice

There have been no reports on the quantitative prediction of CYP3A induction-mediated decreases in AUC and Cmax for drug candidates identified as a "victims" of CYP3A induction. Our previous study separately evaluated the fold-induction of hepatic and intestinal CYP3A by known inducers using clinical induction data and revealed that we were able to quantitatively predict the AUC ratio (AUCR) of a few CYP3A substrates in the presence and absence of CYP3A inducers. In the present study, we investigate the predictability of AUCR and also Cmax ratio (CmaxR) in additional 54 clinical studies. The fraction metabolized by CYP3A (fm), the intestinal bioavailability (Fg), and the hepatic intrinsic clearance (CLint) of substrates were determined by the in vitro experiments as well as clinical data used for calculating AUCR and CmaxR. The result showed that 65-69% and 65-67% of predictions were within 2-fold of observed AUCR and CmaxR, respectively. A simulation using multiple parameter combinations suggested that the variability of fm and Fg within a certain range might have a minimal impact on the calculation output. These findings suggest that clinical AUCR and CmaxR of CYP3A substrates can be quantitatively predicted from the preclinical stage.

Clinical Trial Data-Driven Risk Assessment of Drug-Drug Interactions: A Rapid and Accurate Decision-Making Tool

BACKGROUND

In clinical practice, the vast array of potential drug combinations necessitates swift and accurate assessments of pharmacokinetic drug-drug interactions (DDIs), along with recommendations for adjustments. Current methodologies for clinical DDI evaluations primarily rely on basic extrapolations from clinical trial data. However, these methods are limited in accuracy owing to their lack of a comprehensive consideration of various critical factors, including the inhibitory potency, dosage, and type of the inhibitor, as well as the metabolic fraction and intestinal availability of the substrate.

OBJECTIVE

This study aims to propose an efficient and accurate clinical pharmacokinetic-mediated DDI assessment tool, which comprehensively considers the effects of inhibitory potency and dosage of inhibitors, intestinal availability and fraction metabolized of substrates on DDI outcomes.

METHODS

This study focuses on DDIs caused by cytochrome P450 3A4 enzyme inhibition, utilizing extensive clinical trial data to establish a methodology to calculate the metabolic fraction and intestinal availability for substrates, as well as the concentration and inhibitory potency for inhibitors ( K i ork inact / K I ). These parameters were then used to predict the outcomes of DDIs involving 33 substrates and 20 inhibitors. We also defined the risk index for substrates and the potency index for inhibitors to establish a clinical DDI risk scale. The training set for parameter calculation consisted of 73 clinical trials. The validation set comprised 89 clinical DDI trials involving 53 drugs. which was used to evaluate the reliability of in vivo values of K i andk inact / K I , the accuracy of DDI predictions, and the false-negative rate of risk scale.

RESULTS

First, the reliability of the in vivo K i andk inact / K I values calculated in this study was assessed using a basic static model. Compared with values obtained from other methods, this study values showed a lower geometric mean fold error and root mean square error. Additionally, incorporating these values into the physiologically based pharmacokinetic-DDI model facilitated a good fitting of the C-t curves when the substrate's metabolic enzymes are inhibited. Second, area under the curve ratio predictions of studied drugs were within a 1.5 × margin of error in 81% of cases compared with clinical observations in the validation set. Last, the clinical DDI risk scale developed in this study predicted the actual risks in the validation set with only a 5.6% incidence of serious false negatives.

CONCLUSIONS

This study offers a rapid and accurate approach for assessing the risk of pharmacokinetic-mediated DDIs in clinical practice, providing a foundation for rational combination drug use and dosage adjustments.

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.

Screening of 16 major drug glucuronides for time-dependent inhibition of nine drug-metabolizing CYP enzymes - detailed studies on CYP3A inhibitors

Time-dependent inhibition of cytochrome P450 (CYP) enzymes has been observed for a few glucuronide metabolites of clinically used drugs. Here, we investigated the inhibitory potential of 16 glucuronide metabolites towards nine major CYP enzymes in vitro. Automated substrate cocktail methods were used to screen time-dependent inhibition of CYP1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2J2 and 3A in human liver microsomes. Seven glucuronides (carvedilol β-D-glucuronide, diclofenac acyl-β-D-glucuronide, 4-hydroxyduloxetine β-D-glucuronide, ezetimibe phenoxy-β-D-glucuronide, raloxifene 4'-glucuronide, repaglinide acyl-β-D-glucuronide and valproic acid β-D-glucuronide) caused NADPH- and time-dependent inhibition of at least one of the CYPs investigated, including CYP2A6, CYP2C19 and CYP3A. In more detailed experiments, we focused on the glucuronides of carvedilol and diclofenac, which inhibited CYP3A. Carvedilol β-D-glucuronide showed weak time-dependent inhibition of CYP3A, but the parent drug carvedilol was found to be a more potent inhibitor of CYP3A, with the half-maximal inhibitor concentration (IC50) decreasing from 7.0 to 1.1 µM after a 30-min preincubation with NADPH. The maximal inactivation constant (kinact) and the inhibitor concentration causing half of kinact (KI) for CYP3A inactivation by carvedilol were 0.051 1/min and 1.8 µM, respectively. Diclofenac acyl-β-D-glucuronide caused time-dependent inactivation of CYP3A at high concentrations, with a 4-fold IC50 shift (from 400 to 98 µM after a 30-min preincubation with NADPH) and KI and kinact values of >2,000 µM and >0.16 1/min. In static predictions, carvedilol caused significant (>1.25-fold) increase in the exposure of the CYP3A substrates midazolam and simvastatin. In conclusion, we identified several glucuronide metabolites with CYP inhibitory properties. Based on detailed experiments, the inactivation of CYP3A by carvedilol may cause clinically significant drug-drug interactions.

Evaluation of Icotinib as a Potent and Selective Inhibitor of Aldehyde Oxidase for Reaction Phenotyping in Human Hepatocytes

Aldehyde oxidase (AO) is a molybdenum cofactor-containing cytosolic enzyme that has gained prominence due to its involvement in the developmental failure of several drug candidates in first-in-human trials. Unlike cytochrome P450s (P450) and glucuronosyltransferase, AO substrates have been plagued by poor in vitro to in vivo extrapolation, leading to low systemic exposures and underprediction of human dose. However, apart from measuring a drug's AO clearance rates, it is also important to determine the relative contribution to metabolism by this enzyme (fm,AO). Although hydralazine is the most well-studied time-dependent inhibitor (TDI) of AO and is frequently employed for AO reaction phenotyping in human hepatocytes to derive fm,AO, multiple studies have expressed concerns pertaining to its utility in providing accurate estimates of fm,AO values due to its propensity to significantly inhibit P450s at the concentrations typically used for reaction phenotyping. In this study, we characterized icotinib, a cyclized analog of erlotinib, as a potent TDI of AO-inactivating human liver cytosolic zoniporide 2-oxidation equipotently with erlotinib with a maximal inactivate rate/inactivator concentration at half maximal inactivation rate (K I) ratio of 463 and 501 minute-1mM-1 , respectively. Moreover, icotinib also exhibits selectivity against P450 and elicits significantly weaker inhibition against human liver microsomal UGT1A1/3 as compared with erlotinib. Finally, we evaluated icotinib as an inhibitor of AO for reaction phenotyping in cryopreserved human hepatocytes and demonstrated that it can yield more accurate prediction of fm,AO compared with hydralazine and induce sustained suppression of AO activity at higher cell densities, which will be important for reaction phenotyping endeavors of low clearance drugs SIGNIFICANCE STATEMENT: In this study, we characterized icotinib as a potent time-dependent inhibitor of AO with ample selectivity margins against the P450s and UGT1A1/3 and demonstrated its utility for reaction phenotyping in human hepatocytes to obtain accurate estimates of fm,AO for victim DDI risk predictions. We envisage the adoption of icotinib in place of hydralazine in AO reaction phenotyping.

Prediction of Drug-Drug Interactions with Ensartinib as a Time-Dependent CYP3A Inhibitor Using Physiologically Based Pharmacokinetic Model

Ensartinib (X-396) is a second-generation anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitor (TKI) indicated for the treatment of ALK-positive patients with locally advanced or metastatic non-small cell lung cancer. Although in vitro experiments and molecular docking suggested its potential as a cytochrome P450 inhibitor, no further investigation or clinical trials have been conducted to assess its drug-drug interaction (DDI) risk. In this study, we conducted a series of in vitro experiments to elucidate the inhibition mechanism of ensartinib. Furthermore, a physiologically-based pharmacokinetic (PBPK) model was developed based on in vitro, in silico, and in vivo parameters, verified using clinical data, and applied to predict the clinical DDI mediated by ensartinib. The in vitro incubation experiments suggested that ensartinib exhibited strong time-dependent inhibition. Simulation results from the PBPK model indicated a significant increase in the exposure of CYP3A substrates in the presence of ensartinib, with the maximal plasma concentration and area under the plasma concentration-time curve increasing up to 12-fold and 29-fold for sensitive substrates. Based on these findings, it is evident that co-administration of ensartinib and CYP3A substrates requires careful regulatory consideration. SIGNIFICANCE STATEMENT: Ensartinib was found to be a strong time-dependent inhibitor of CYP3A for the first time based on in vitro experiments, but there was no research conducted to estimate the risk of drug-drug interaction (DDI) of ensartinib in clinic. Therefore, the first ensartinib physiologically based pharmacokinetic model was developed and applied to predict various untested scenarios. The simulation result indicated that the exposure of CYP3A substrate increased significantly and urged the further clinical DDI study.

Biotransformation research advances - 2022 year in review

This annual review is the eighth of its kind since 2016 (Baillie et al. 2016, Khojasteh et al. 2017, Khojasteh et al. 2018, Khojasteh et al. 2019, Khojasteh et al. 2020, Khojasteh et al. 2021, Khojasteh et al. 2022). Our objective is to explore and share articles which we deem influential and significant in the field of biotransformation.

Pharmacokinetic and Pharmacodynamic Drug-Drug Interactions: Research Methods and Applications

Because of the high research and development cost of new drugs, the long development process of new drugs, and the high failure rate at later stages, combining past drugs has gradually become a more economical and attractive alternative. However, the ensuing problem of drug-drug interactions (DDIs) urgently need to be solved, and combination has attracted a lot of attention from pharmaceutical researchers. At present, DDI is often evaluated and investigated from two perspectives: pharmacodynamics and pharmacokinetics. However, in some special cases, DDI cannot be accurately evaluated from a single perspective. Therefore, this review describes and compares the current DDI evaluation methods based on two aspects: pharmacokinetic interaction and pharmacodynamic interaction. The methods summarized in this paper mainly include probe drug cocktail methods, liver microsome and hepatocyte models, static models, physiologically based pharmacokinetic models, machine learning models, in vivo comparative efficacy studies, and in vitro static and dynamic tests. This review aims to serve as a useful guide for interested researchers to promote more scientific accuracy and clinical practical use of DDI studies.

A linked physiologically based pharmacokinetic model for hydroxychloroquine and metabolite desethylhydroxychloroquine in SARS-CoV-2(-)/(+) populations

Hydroxychloroquine (HCQ) is Food and Drug Administration (FDA)-approved for malaria, systemic and chronic discoid lupus erythematosus, and rheumatoid arthritis. Because HCQ has a proposed multimodal mechanism of action and a well-established safety profile, it is often investigated as a repurposed therapeutic for a range of indications. There is a large degree of uncertainty in HCQ pharmacokinetic (PK) parameters which complicates dose selection when investigating its use in new disease states. Complications with HCQ dose selection emerged as multiple clinical trials investigated HCQ as a potential therapeutic in the early stages of the COVID-19 pandemic. In addition to uncertainty in baseline HCQ PK parameters, it was not clear if disease-related consequences of SARS-CoV-2 infection/COVID-19 would be expected to impact the PK of HCQ and its primary metabolite desethylhydroxychloroquine (DHCQ). To address the question whether SARS-CoV-2 infection/COVID-19 impacted HCQ and DHCQ PK, dried blood spot samples were collected from SARS-CoV-2(-)/(+) participants administered HCQ. When a previously published physiologically based pharmacokinetic (PBPK) model was used to fit the data, the variability in exposure of HCQ and DHCQ was not adequately captured and DHCQ concentrations were overestimated. Improvements to the previous PBPK model were made by incorporating the known range of blood to plasma concentration ratios (B/P) for each compound, adjusting HCQ and DHCQ distribution settings, and optimizing DHCQ clearance. The final PBPK model adequately captured the HCQ and DHCQ concentrations observed in SARS-CoV-2(-)/(+)participants, and incorporating COVID-19-associated changes in cytochrome P450 activity did not further improve model performance for the SARS-CoV-2(+) population.

Discovery of the Potent and Selective MC4R Antagonist PF-07258669 for the Potential Treatment of Appetite Loss

The melanocortin-4 receptor (MC4R) is a centrally expressed, class A GPCR that plays a key role in the regulation of appetite and food intake. Deficiencies in MC4R signaling result in hyperphagia and increased body mass in humans. Antagonism of MC4R signaling has the potential to mitigate decreased appetite and body weight loss in the setting of anorexia or cachexia due to underlying disease. Herein, we report on the identification of a series of orally bioavailable, small-molecule MC4R antagonists using a focused hit identification effort and the optimization of these antagonists to provide clinical candidate 23. Introduction of a spirocyclic conformational constraint allowed for simultaneous optimization of MC4R potency and ADME attributes while avoiding the production of hERG active metabolites observed in early series leads. Compound 23 is a potent and selective MC4R antagonist with robust efficacy in an aged rat model of cachexia and has progressed into clinical trials.

Can Mechanistic Static Models for Drug-Drug Interactions Support Regulatory Filing for Study Waivers and Label Recommendations?

BACKGROUND AND OBJECTIVE

Mechanistic static and dynamic physiologically based pharmacokinetic models are used in clinical drug development to assess the risk of drug-drug interactions (DDIs). Currently, the use of mechanistic static models is restricted to screening DDI risk for an investigational drug, while dynamic physiologically based pharmacokinetic models are used for quantitative predictions of DDIs to support regulatory filing. As physiologically based pharmacokinetic model development by sponsors as well as a review of models by regulators require considerable resources, we explored the possibility of using mechanistic static models to support regulatory filing, using representative cases of successful physiologically based pharmacokinetic submissions to the US Food and Drug Administration under different classes of applications.

METHODS

Drug-drug interaction predictions with mechanistic static models were done for representative cases in the different classes of applications using the same data and modelling workflow as described in the Food and Drug Administration clinical pharmacology reviews. We investigated the hypothesis that the use of unbound average steady-state concentrations of modulators as driver concentrations in the mechanistic static models should lead to the same conclusions as those from physiologically based pharmacokinetic modelling for non-dynamic measures of DDI risk assessment such as the area under the plasma concentration-time curve ratio, provided the same input data are employed for the interacting drugs.

RESULTS

Drug-drug interaction predictions of area under the plasma concentration-time curve ratios using mechanistic static models were mostly comparable to those reported in the Food and Drug Administration reviews using physiologically based pharmacokinetic models for all representative cases in the different classes of applications.

CONCLUSIONS

The results reported in this study should encourage the use of models that best fit an intended purpose, limiting the use of physiologically based pharmacokinetic models to those applications that leverage its unique strengths, such as what-if scenario testing to understand the effect of dose staggering, evaluating the role of uptake and efflux transporters, extrapolating DDI effects from studied to unstudied populations, or assessing the impact of DDIs on the exposure of a victim drug with concurrent mechanisms. With this first step, we hope to trigger a scientific discussion on the value of a routine comparison of the two methods for regulatory submissions to potentially create a best practice that could help identify examples where the use of dynamic changes in modulator concentrations could make a difference to DDI risk assessment.

Interspecies evaluation of a physiologically based pharmacokinetic model to predict the biodistribution dynamics of dendritic nanoparticles

The exposure of a dendritic nanoparticle and its conjugated active pharmaceutical ingredient (API) was determined in mouse, rat and dog, with the aim of investigating interspecies differences facilitating clinical translation. Plasma area under the curves (AUCs) were found to be dose proportional across species, while dose normalized concentration time course profiles in plasma, liver and spleen were superimposable in mouse, rat and dog. A physiologically based pharmacokinetic (PBPK) model, previously developed for mouse, was evaluated as a suitable framework to prospectively capture concentration dynamics in rat and dog. The PBPK model, parameterized either by considering species-specific physiology or using alternate scaling methods such as allometry, was shown to capture exposure profiles across species. A sensitivity analysis highlighted API systemic clearance as a key parameter influencing released API levels. The PBPK model was utilized to simulate human exposure profiles, which overlaid dose-normalized data from mouse, rat and dog. The consistency in measured interspecies exposures as well as the capability of the PBPK model to simulate observed dynamics support its use as a powerful translational tool.

Epigenetics in drug disposition & drug therapy: symposium report of the 24th North American meeting of the International Society for the Study of Xenobiotics (ISSX)

The 24th North American International Society for the Study of Xenobiotics (ISSX) meeting, held virtually from September 13 to 17, 2021, embraced the theme of "Broadening Our Horizons." This reinforces a key mission of ISSX: striving to share innovative science related to drug discovery and development. Session speakers and the ISSX New Investigators Group, which supports the scientific and professional development of student and early career ISSX members, elected to highlight the scientific content presented during the captivating session titled, "Epigenetics in Drug Disposition & Drug Therapy." The impact genetic variation has on drug response is well established; however, this session underscored the importance of investigating the role of epigenetics in drug disposition and drug discovery. Session speakers, Drs. Ning, McClay, and Lazarus, detailed mechanisms by which epigenetic players including long non-coding RNA (lncRNAs), microRNA (miRNAs), DNA methylation, and histone acetylation can alter the expression of genes involved in pharmacokinetics, pharmacodynamics, and toxicity. Dr. Ning detailed current knowledge about miRNAs and lncRNAs and the mechanisms by which they can affect the expression of drug metabolizing enzymes (DMEs) and nuclear receptors. Dr. Lazarus discussed the potential role of miRNAs on UDP-glucuronosyltransferase (UGT) expression and activity. Dr. McClay provided evidence that aging alters methylation and acetylation of DMEs in the liver, affecting gene expression and activity. These topics, compiled by the symposium organizers, presenters, and the ISSX New Investigators Group, are herein discussed, along with exciting future perspectives for epigenetics in drug disposition and drug discovery research.

Biotransformation Novel Advances - 2021 year in review

Biotransformation field is constantly evolving with new molecular structures and discoveries of metabolic pathways that impact efficacy and safety. Recent review by Kramlinger et al (2022) nicely captures the future (and the past) of highly impactful science of biotransformation (see the first article). Based on the selected articles, this review was categorized into three sections: (1) new modalities biotransformation, (2) drug discovery biotransformation, and (3) drug development biotransformation (Table 1).

Prediction of CYP‐mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator

Physiologically‐based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug–drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism‐based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (C_max) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both C_max and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP‐mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

Comprehensive Predictions of Cytochrome P450 (P450)-Mediated In Vivo Cannabinoid-Drug Interactions Based on Reversible and Time-Dependent P450 Inhibition in Human Liver Microsomes

We previously reported the unbound reversible (IC50,u) and time-dependent (KI,u) inhibition potencies of cannabidiol (CBD), delta-9-tetrahydrocannabinol (THC), and THC metabolites 11-hydroxy THC (11-OH THC) and 11-nor-9-carboxy-delta-9-THC (11-COOH THC) against the major cytochrome P450 (P450) enzymes (1A2, 2C9, 2C19, 2D6, and 3A). Here, using human liver microsomes, we determined the CYP2A6, 2B6, and 2C8 IC50,u values of the aforementioned cannabinoids and the IC50,u and KI,u of the circulating CBD metabolites 7-hydroxy CBD (7-OH CBD) and 7-carboxy CBD (7-COOH CBD), against all the P450s listed above. The IC50,u of CBD, 7-OH CBD, THC, and 11-OH THC against CYP2B6 was 0.05, 0.34, 0.40, and 0.32 μM, respectively, and against CYP2C8 was 0.28, 1.02, 0.67, and 3.66 μM, respectively. 7-COOH CBD, but not 11-COOH THC, was a weak inhibitor of CYP2B6 and 2C8. All tested cannabinoids except 11-COOH THC were weak inhibitors of CYP2A6. 7-OH CBD inhibited all P450s examined (IC50,u<2.5 μM) except CYP1A2 and inactivated CYP2C19 and CYP3A, with inactivation efficiencies (kinact/KI,u) of 0.10 and 0.14 minutes-1 μM-1, respectively. Using several different static models, we predicted the following maximum pharmacokinetic interactions (affected P450 probe drug and area under the plasma concentration-time curve ratio) between oral CBD (700 mg) and drugs predominantly metabolized by CYP3A (midazolam, 14.8) > 2C9 (diclofenac, 9.6) > 2C19 (omeprazole, 7.3) > 1A2 (theophylline, 4.0) > 2B6 (ticlopidine, 2.2) > 2D6 (dextromethorphan, 2.1) > 2C8 (repaglinide, 1.6). Oral (130 mg) or inhaled (75 mg) THC was predicted to precipitate interactions with drugs predominately metabolized by CYP2C9 (diclofenac, 6.6 or 2.3, respectively) > 3A (midazolam, 1.8) > 1A2 (theophylline, 1.4). In vivo drug interaction studies are warranted to verify these predictions. SIGNIFICANCE STATEMENT: This study, combined with our previous findings, provides for the first time a comprehensive analysis of the potential for cannabidiol, delta-9-tetrahydrocannabinol, and their metabolites to inhibit cytochrome P450 enzymes in a reversible or time-dependent manner. These analyses enabled us to predict the potential of these cannabinoids to produce drug interactions in vivo at clinical or recreational doses.

Characterization of Correction Factors to Enable Assessment of Clinical Risk from In Vitro CYP3A4 Induction Data and Basic Drug-Drug Interaction Models

Background and Objective

Induction of drug-metabolizing enzymes can lead to drug-drug interactions (DDIs); therefore, early assessment is often conducted. Previous reports focused on true positive cytochrome P450 3A (CYP3A) inducers leaving a gap in translation for in vitro inducers which do not manifest in clinical induction. The goal herein was to expand the in vitro induction dataset by including true negative clinical inducers to identify a correction factor to basic DDI models, which reduces false positives without impacting false negatives.

Methods

True negative clinical inducers were identified through a literature search, in vitro induction parameters were generated in three human hepatocyte donors, and the performance of basic induction models proposed by regulatory agencies, concentration producing twofold induction (F2), basic static model (R3) and relative induction score (RIS), was used to characterize clinical induction risk.

Results

The data demonstrated the importance of correcting for in vitro binding and metabolism to derive induction parameters. The aggregate analysis indicates that the RIS with a positive cut-off of < 0.7-fold area under the curve ratio (AUCR) provides the best quantitative prediction. Additionally, correction factors of ten and two times the unbound peak plasma concentration at steady state (C_max,ss,u) can be confidently used to identify true positive inducers when referenced against the concentration resulting in twofold increase in messenger ribonucleic acid (mRNA) or using the R3 equation, respectively.

Conclusions

These iterative improvements, which reduce the number of false positives, could aid regulatory recommendations and limit unnecessary clinical explorations into CYP3A induction.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13318-022-00763-y.

Predictive In Vitro-In Vivo Extrapolation for Time Dependent Inhibition of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6 Using Pooled Human Hepatocytes, Human Liver Microsomes, and a Simple Mechanistic Static Model

Inactivation of Cytochrome P450 (CYP450) enzymes can lead to significant increases in exposure of co-medicants. The majority of reported in vitro to in vivo extrapolation (IVIVE) data have historically focused on CYP3A4 leaving the assessment of other CYP isoforms insubstantial. To this end, the utility of human hepatocytes (HHEP) and microsome (HLM) to predict clinically relevant DDIs was investigated with a focus on CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6. Evaluation of IVIVE for CYP2B6 was limited to only weak inhibition. A search of the University of Washington Drug-Drug Interaction Database was conducted to identify a clinically relevant weak, moderate and strong inhibitor for selective substrates of CYP1A2, CYP2C8, CYP2C9, CYP2C19 and CYP2D6, resulting in 18 inhibitors for in vitro characterization against 119 clinical interaction studies. Pooled human hepatocytes and HLM were pre-incubated with increasing concentrations of inhibitors for designated timepoints. Time dependent inhibition (TDI) was detected in HLM for four moderate/strong inhibitors suggesting that some optimization of incubation conditions (i.e. lower protein concentrations) is needed to capture weak inhibition. Clinical risk assessment was conducted by incorporating the in vitro derived kinetic parameters k_inact and K_I into static equations recommended by regulatory authorities. Significant overprediction was observed when applying the basic models recommended by regulatory agencies. Mechanistic static models (MSM), which consider the fraction of metabolism through the impacted enzyme, using the unbound hepatic inlet concentration lead to the best overall prediction accuracy with 92% and 85% of data from HHEPs and HLM, respectively, within 2-fold of the observed value. Significance Statement Collectively, the data demonstrate that coupling time-dependent inactivation parameters derived from pooled human hepatocytes and HLM with a mechanistic static model provides an easy and quantitatively accurate means to determine clinical DDI risk from in vitro data. Weak and moderate inhibitors did not show TDI under standard incubation conditions using HLM and optimization of incubation conditions is warranted. Recommendations are made with respect to input parameters for IVIVE of TDI with non-CYP3A enzymes using available data from HLM and HHEPs.