Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions with Efavirenz Involving Simultaneous Inducing and Inhibitory Effects on Cytochromes

Quantitative clinical risk assessment of CYP2C, UDP-glucuronosyltransferase, P-glycoprotein induction, and complex drug-drug interactions using TruVivo human hepatocyte triculture platform

Quantitative prediction and clinical risk assessment for induction of drug-metabolizing enzymes and transporters beyond CYP3A has been hindered by low dynamic response in the gold standard hepatocyte monoculture model. A gap in translation of the drug-drug interaction (DDI) potential of a compound is particularly apparent when an inducer also inhibits CYP3A, leading to uncertainty in the potential net clinical outcome for CYP3A substrates. In addition, enzymes such as CYP2C8, CYP2C9, CYP2C19, UGT1A4, and P-glycoprotein, which are coregulated with CYP3A, may result in clinically relevant induction that cannot be derisked by conducting clinical interaction studies with CYP3A substrates. Identification of an in vitro model that demonstrates consistent and well defined induction of enzymes and transporters beyond CYP3A would open the opportunity to avoid unnecessary clinical interaction studies and subsequently have high value in the drug discovery and development toolbox. The TruVivo model is a novel all-human primary cell model, containing hepatocytes plus stromal and epithelial feeder cells. Within these studies, the TruVivo model was validated as a predictive tool for clinical risk assessment for induction of CYP2C8, CYP2C9, CYP2C19, CYP3A4, UGT1A4, and P-glycoprotein using known clinical inducers. Exploration into the utility of TruVivo to delineate complex DDI involving coinducers/inhibitors was also conducted and showed immense opportunity, demonstrating the value of in situ DDI experiments when clinically relevant levels of precipitant and object drugs are used. These data highlight the potential of this in vitro tool to model induction and complex DDI. SIGNIFICANCE STATEMENT: Clinical risk assessment for induction of enzymes and transporters coregulated with CYP3A, including the CYP2C enzymes, UDP-glucuronosyltransferases, and P-gp has been hampered by the low dynamic response of available in vitro models. These studies aimed to validate a novel all-human hepatocyte model, TruVivo, as a predictive tool for induction-based DDI. In addition, the model was evaluated for in situ prediction of complex DDI and shows promise in predicting net clinical outcomes for several inducers/inhibitors against selective and nonselective CYP3A substrates.

Effect of a moderate CYP3A inducer efavirenz on the pharmacokinetics of fuzuloparib: An open-label, fixed sequence study in Chinese healthy male subjects

To evaluate the potential drug-drug interaction (DDI), safety and tolerability of fuzuloparib co-administered with a moderate CYP3A inducer efavirenz in healthy male subjects. Eighteen healthy male subjects were enrolled in a single-center, single-arm, open-label, fixed-sequence study. Fuzuloparib was administered as a single oral 50 mg under a fasting state on day 1, efavirenz (600 mg once daily) was given on days 4-17 before bed time, concomitantly with fuzuloparib on day 18, and for the follow-up 3 additional days (days 19-20). Pharmacokinetic sampling was performed following each fuzuloparib dose. Safety and tolerability were assessed during the whole process via clinical laboratory tests. Ratios of least-squares means (GMRs) and 90% geometric confidence interval (90% CI) of maximum plasma concentration (C_max), the area under the curve of plasma concentration-time from zero to the last measurable concentration (AUC_0 - t) and the area under the curve of blood concentration from zero to infinity (AUC_0-∞) for fuzuloparib combined with efavirenz to fuzuloparib alone were 0.473 (0.394, 0.568), 0.220 (0.185, 0.263) and 0.221 (0.185, 0.263), respectively. Co-administration with efavirenz led to 53% and 78% decreases in fuzuloparib C_max and AUC_0-∞. All 18 subjects enrolled in this study were included in the safety analysis set. A total of 16 subjects had 62 AEs during the study period. No serious adverse events (SAE) were reported. Most treatment-emergent adverse events were grade 1 or 2 based on CTCAE. Only one grade 3 adverse event was observed. Concomitant intake of fuzuloparib with the moderate CYP3A inhibitor efavirenz resulted in a decrease in fuzuloparib AUC_0-∞ and C_max of 78% and 53% respectively. The results suggested that concomitant moderate CYP3A inducers should be avoided during the administration of fuzuloparib, or else the dosage adjustments should be required. (This trial was registered at http://www.chinadrugtrials.org.cn . The registration No. is CTR20211022, and the date of registration is 2021-05-13).

Pharmacokinetic Drug-Drug Interactions Involving Antiretroviral Agents: An Update

Antiretroviral therapy is the recognized treatment for human immunodeficiency virus (HIV) infection involving several antiviral agents. Even though highly active antiretroviral therapy has been proven to be very effective in suppressing HIV replication, the antiretroviral drugs, belonging to different pharmacological classes, present quite complex pharmacokinetic properties such as extensive drug metabolism and transport by membrane-associated drug carriers. Moreover, due to uncomplications or complications in HIV-infected populations, an antiretroviralbased multiple-drug coadministration therapy strategy is usually applied for treatment effect, thus raising the possibility of drug-drug interactions between antiretroviral drugs and common drugs such as opioids, stains, and hormonal contraceptives. Herein, thirteen classical antiretroviral drugs approved by US Food and Drug Administration were summarized. Besides, relative drug metabolism enzymes and transporters known to interact with those antiretroviral drugs were detailed and described. Furthermore, one after the summarized antiretroviral drugs, the drug-drug interactions between two antiretroviral drugs or antiretroviral drug - conventional medical drugs of the past decade were discussed and summarized. This review is intended to deepen the pharmacological understanding of antiretroviral drugs and promote more secure clinical applications for antiretroviral drugs to treat HIV.

Pharmacometrics: The Already-Present Future of Precision Pharmacology

The use of mathematical modeling to represent, analyze, make predictions or providing information on data obtained in drug research and development has made pharmacometrics an area of great prominence and importance. The main purpose of pharmacometrics is to provide information relevant to the search for efficacy and safety improvements in pharmacotherapy. Regulatory agencies have adopted pharmacometrics analysis to justify their regulatory decisions, making those decisions more efficient. Demand for specialists trained in the field is therefore growing. In this review, we describe the meaning, history, and development of pharmacometrics, analyzing the challenges faced in the training of professionals. Examples of applications in current use, perspectives for the future, and the importance of pharmacometrics for the development and growth of precision pharmacology are also presented.

Predicting Drug-Drug Interactions between Rifampicin and Ritonavir-Boosted Atazanavir Using PBPK Modelling

Objectives

The aim of this study was to simulate the drug–drug interaction (DDI) between ritonavir-boosted atazanavir (ATV/r) and rifampicin (RIF) using physiologically based pharmacokinetic (PBPK) modelling, and to predict suitable dose adjustments for ATV/r for the treatment of people living with HIV (PLWH) co-infected with tuberculosis.

Methods

A whole-body DDI PBPK model was designed using Simbiology 9.6.0 (MATLAB R2019a) and verified against reported clinical data for all drugs administered alone and concomitantly. The model contained the induction mechanisms of RIF and ritonavir (RTV), the inhibition effect of RTV for the enzymes involved in the DDI, and the induction and inhibition mechanisms of RIF and RTV on the uptake and efflux hepatic transporters. The model was considered verified if the observed versus predicted pharmacokinetic values were within twofold. Alternative ATV/r dosing regimens were simulated to achieve the trough concentration (C_trough) clinical cut-off of 150 ng/mL.

Results

The PBPK model was successfully verified according to the criteria. Simulation of different dose adjustments predicted that a change in regimen to twice-daily ATV/r (300/100 or 300/200 mg) may alleviate the induction effect of RIF on ATV C_trough, with > 95% of individuals predicted to achieve C_trough above the clinical cut-off.

Conclusions

The developed PBPK model characterized the induction-mediated DDI between RIF and ATV/r, accurately predicting the reduction of ATV plasma concentrations in line with observed clinical data. A change in the ATV/r dosing regimen from once-daily to twice-daily was predicted to mitigate the effect of the DDI on the C_trough of ATV, maintaining plasma concentration levels above the therapeutic threshold for most patients.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40262-021-01067-1.

Physiologically Based Pharmacokinetic Modelling to Identify Pharmacokinetic Parameters Driving Drug Exposure Changes in the Elderly

BACKGROUND

Medication use is highly prevalent with advanced age, but clinical studies are rarely conducted in the elderly, leading to limited knowledge regarding age-related pharmacokinetic changes.

OBJECTIVE

The objective of this study was to investigate which pharmacokinetic parameters determine drug exposure changes in the elderly by conducting virtual clinical trials for ten drugs (midazolam, metoprolol, lisinopril, amlodipine, rivaroxaban, repaglinide, atorvastatin, rosuvastatin, clarithromycin and rifampicin) using our physiologically based pharmacokinetic (PBPK) framework.

METHODS

PBPK models for all ten drugs were developed in young adults (20-50 years) following the best practice approach, before predicting pharmacokinetics in the elderly (≥ 65 years) without any modification of drug parameters. A descriptive relationship between age and each investigated pharmacokinetic parameter (peak concentration [C_max], time to C_max [t_max], area under the curve [AUC], clearance, volume of distribution, elimination-half-life) was derived using the final PBPK models, and verified with independent clinically observed data from 52 drugs.

RESULTS

The age-related changes in drug exposure were successfully simulated for all ten drugs. Pharmacokinetic parameters were predicted within 1.25-fold (70%), 1.5-fold (86%) and 2-fold (100%) of clinical data. AUC increased progressively by 0.9% per year throughout adulthood from the age of 20 years, which was explained by decreased clearance, while C_max, t_max and volume of distribution were not affected by human aging. Additional clinical data of 52 drugs were contained within the estimated variability of the established age-dependent correlations for each pharmacokinetic parameter.

CONCLUSION

The progressive decrease in hepatic and renal blood flow, as well as glomerular filtration, rate led to a reduced clearance driving exposure changes in the healthy elderly, independent of the drug.

Drug-Drug Interaction Between Oxycodone and Diazepam by a Combined in Silico Pharmacokinetic and Pharmacodynamic Modeling Approach

Opioids and benzodiazepines have complex drug-drug interactions (DDIs), which serve as an important source of adverse drug effects. In this work, we predicted the DDI between oxycodone (OXY) and diazepam (DZP) in the human body by applying in silico pharmacokinetic (PK) and pharmacodynamic (PD) modeling and simulation. First, we studied the PK interaction between OXY and DZP with a physiologically based pharmacokinetic (PBPK) model. Second, we applied molecular modeling techniques including molecular docking, molecular dynamics (MD) simulation, and the molecular mechanics/Poisson-Boltzmann surface area (MM-PBSA) free energy method to predict the PD-DDI between these two drugs. The PK interaction between OXY and DZP predicted by the PBPK model was not obvious. No significant interaction was observed between the two drugs at normal doses, though very high doses of DZP demonstrated a non-negligible inhibitory effect on OXY metabolism. On the contrary, the molecular modeling study shows that DZP has potential to compete with OXY at the same binding pocket of the active μ-opioid receptor (MOR) and κ-opioid receptor (KOR). MD simulation and MM-PBSA calculation results demonstrated that there is likely a synergetic effect between OXY and DZP binding to opioid receptors, as OXY is likely to target the active MOR while DZP selectively binds to the active KOR. Thus, pharmacokinetics contributes slightly to the DDI between OXY and DZP although an overdose of DZP has been brought to attention. Pharmacodynamics is likely to play a more important role than pharmacokinetics in revealing the mechanism of DDI between OXY and DZP.

Inhibition and induction of CYP enzymes in humans: an update

The cytochrome P450 (CYP) enzyme family is the most important enzyme system catalyzing the phase 1 metabolism of pharmaceuticals and other xenobiotics such as herbal remedies and toxic compounds in the environment. The inhibition and induction of CYPs are major mechanisms causing pharmacokinetic drug–drug interactions. This review presents a comprehensive update on the inhibitors and inducers of the specific CYP enzymes in humans. The focus is on the more recent human in vitro and in vivo findings since the publication of our previous review on this topic in 2008. In addition to the general presentation of inhibitory drugs and inducers of human CYP enzymes by drugs, herbal remedies, and toxic compounds, an in-depth view on tyrosine-kinase inhibitors and antiretroviral HIV medications as victims and perpetrators of drug–drug interactions is provided as examples of the current trends in the field. Also, a concise overview of the mechanisms of CYP induction is presented to aid the understanding of the induction phenomena.

Innovative Approaches for Pharmacology Studies in Pregnant and Lactating Women: A Viewpoint and Lessons from HIV

Medication use during pregnancy in the absence of pharmacokinetic and safety data is common, particularly for antiretrovirals, as pregnant women are not usually included in clinical trials leading to drug licensure. To date, data are typically generated through opportunistic pregnancy studies performed in the postmarketing setting, leading to a substantial time-lag between initial regulatory approval of a drug and availability of essential pregnancy-specific pharmacokinetic and safety data. During this period, health care providers lack key information on human placental transfer, fetal exposure, optimal maternal dosing in pregnancy, and maternal and fetal drug toxicity, including teratogenicity risk. We discuss new approaches that could facilitate the acquisition of these critical data earlier in the drug development process, aiding clinicians and patients in making informed decisions on drug selection and dosing during pregnancy. An integrated approach utilizing multiple novel methodologies (in vitro, ex vivo, in silico and in vivo) is needed to accelerate the availability of pharmacology data in pregnancy and lactation.

Effect of patient genetics on etonogestrel pharmacokinetics when combined with efavirenz or nevirapine ART

BACKGROUND

We previously demonstrated that etonogestrel concentrations were 82% lower in women using etonogestrel contraceptive implants plus efavirenz-based ART compared with women not receiving ART.

OBJECTIVES

To investigate the genetic contribution to this previously observed drug-drug interaction through studying SNPs in genes known to be involved in efavirenz, nevirapine or etonogestrel metabolism in the same group of women.

PATIENTS AND METHODS

Here, we present a secondary analysis evaluating SNPs involved in efavirenz, nevirapine and etonogestrel metabolism and associated etonogestrel pharmacokinetics among 57 women, 19 not receiving ART (control group), 19 receiving efavirenz- (600 mg daily) based ART and 19 receiving nevirapine- (200 mg twice daily) based ART. Associations between patient genotype and etonogestrel pharmacokinetic parameters were determined through univariate and multivariate linear regression. This study was registered at clinicaltrials.gov (NCT02082652).

RESULTS

Within the control group, CYP2B6 983 T>C was associated with 27% higher etonogestrel Cmax and 28% higher AUC0-24weeks. In the efavirenz group CYP2B6 516 G>T was associated with 43% lower etonogestrel Cmin and 34% lower AUC0-24weeks. For participants receiving nevirapine, NR1I2 63396 C>T was associated with 39% lower etonogestrel Cmin and 37% lower AUC0-24weeks.

CONCLUSIONS

This study demonstrates the influence of pharmacogenetics on the extent of drug-drug interactions between etonogestrel and efavirenz- or nevirapine-based ART. Efavirenz plus the etonogestrel contraceptive implant results in a detrimental drug-drug interaction irrespective of patient genetics, which is worsened in women possessing variant alleles for these CYP2B6 SNPs.

Utilization of Physiologically Based Pharmacokinetic Modeling in Clinical Pharmacology and Therapeutics: an Overview

The purpose of this review was to assess the advancement of applications for physiologically based pharmacokinetic (PBPK) modeling in various therapeutic areas. We conducted a PubMed search, and 166 articles published between 2012 and 2018 on FDA-approved drug products were selected for further review. Qualifying publications were summarized according to therapeutic area, medication(s) studied, pharmacokinetic model type utilized, simulator program used, and the applications of that modeling. The results showed a 13-fold increase in the number of papers published from 2012 to 2018, with the largest proportion of articles dedicated to the areas of infectious diseases, oncology, and neurology, and application extensions including prediction of drug-drug interactions due to metabolism and/or transporter-mediated effects and understanding drug kinetics in special populations. In addition, we profiled several high-impact studies whose results were used to guide package insert information and formulate dose recommendations. These results show that while utilization of PBPK modeling has drastically increased over the past several years, regulatory support, lack of easy-to-use systems for clinicians, and challenges with model validation remain major challenges for the widespread adoption of this practice in institutional and ambulatory settings. However, PBPK modeling will continue to be a useful tool in the future to assess therapeutic drug monitoring and the growing field of personalized medicine.

The Segregated Intestinal Flow Model (SFM) for Drug Absorption and Drug Metabolism: Implications on Intestinal and Liver Metabolism and Drug-Drug Interactions

The properties of the segregated flow model (SFM), which considers split intestinal flow patterns perfusing an active enterocyte region that houses enzymes and transporters (<20% of the total intestinal blood flow) and an inactive serosal region (>80%), were compared to those of the traditional model (TM), wherein 100% of the flow perfuses the non-segregated intestine tissue. The appropriateness of the SFM model is important in terms of drug absorption and intestinal and liver drug metabolism. Model behaviors were examined with respect to intestinally (M1) versus hepatically (M2) formed metabolites and the availabilities in the intestine (F_I) and liver (F_H) and the route of drug administration. The %contribution of the intestine to total first-pass metabolism bears a reciprocal relation to that for the liver, since the intestine, a gateway tissue, regulates the flow of substrate to the liver. The SFM predicts the highest and lowest M1 formed with oral (po) and intravenous (iv) dosing, respectively, whereas the extent of M1 formation is similar for the drug administered po or iv according to the TM, and these values sit intermediate those of the SFM. The SFM is significant, as this drug metabolism model explains route-dependent intestinal metabolism, describing a higher extent of intestinal metabolism with po versus the much reduced or absence of intestinal metabolism with iv dosing. A similar pattern exists for drug–drug interactions (DDIs). The inhibitor or inducer exerts its greatest effect on victim drugs when both inhibitor/inducer and drug are given po. With po dosing, more drug or inhibitor/inducer is brought into the intestine for DDIs. The bypass of flow and drug to the enterocyte region of the intestine after intravenous administration adds complications to in vitro–in vivo extrapolations (IVIVE).

Predicting Drug-Drug Interactions Between Rifampicin and Long-Acting Cabotegravir and Rilpivirine Using Physiologically Based Pharmacokinetic Modeling

BACKGROUND

Cabotegravir and rilpivirine are 2 long-acting (LA) antiretrovirals that can be administered intramuscularly; their interaction with rifampicin, a first-line antituberculosis agent, has not been investigated. The aim of this study was to simulate and predict drug-drug interactions (DDIs) between these LA antiretroviral agents and rifampicin using physiologically based pharmacokinetic (PBPK) modeling.

METHODS

The designed PBPK models were qualified (according to European Medicines Agency guidelines) against observed data for oral formulations of cabotegravir, rilpivirine, and rifampicin. Induction potential of rifampicin was also qualified by comparing the DDI between oral cabotegravir and oral rilpivirine with rifampicin. Qualified PBPK models were utilized for pharmacokinetic prediction of DDIs.

RESULTS

PBPK models predicted a reduction in both area under the curve (AUC0-28 days) and trough concentration (Ctrough, 28th day) of LA cabotegravir of 41%-46% for the first maintenance dose coadministered with 600 mg once-daily oral rifampicin. Rilpivirine concentrations were predicted to decrease by 82% for both AUC0-28 days and Ctrough, 28th day following the first maintenance dose when coadministered with rifampicin.

CONCLUSIONS

The developed PBPK models predicted the theoretical effect of rifampicin on cabotegravir and rilpivirine LA intramuscular formulations. According to these simulations, it is likely that coadministration of rifampicin with these LA formulations will result in subtherapeutic concentrations of both drugs.

Theoretical consideration of the properties of intestinal flow models on route-dependent drug removal: Segregated Flow (SFM) vs. Traditional (TM)

The intestine is endowed with a plethora of enzymes and transporters and regulates the flow of substrate to the liver. Physiologically-based pharmacokinetic models have surfaced to describe intestinal removal. The traditional model (TM) describes the intestinal flow as a whole perfusing the entire tissue that contains the intestinal transporters and enzymes. The segregated flow model (SFM) describes that only a fraction (f_Q  < 0.2) of the intestinal blood flow perfuses the enterocyte region where the intestinal enzymes and transporters are housed, rendering a lower drug distribution/intestinal clearance when drug enters via the circulation than from the gut lumen. As shown by simulations, a higher intestinal clearance and extraction ratio (E_I,iv ) exists for the TM than for SFM after iv dosing. By contrast, the E_I,po after po dosing is higher for the SFM, due to the smaller volume of distribution for the enterocyte region and a lower flow rate that result in increased mean residence time and higher drug extraction. Under MBI (mechanism-based inhibition), the AUC_R,po after oral bolus is the highest for drug when inhibitor is given orally, with SFM > TM. Competitive inhibition of intestinal enzymes leads to higher liver metabolism; again, when both drug and inhibitor are given orally, changes in the SFM > TM. However, less definitive patterns result with inhibition of both intestinal and liver enzymes. In conclusion, differences exist for E_I and drug-drug interaction (DDI) between the TM and SFM. The fractional intestinal blood flow (f_Q ) is a key factor affecting different extents of intestinal/liver metabolism of the drug after oral as well as intravenous administration.

Physiologically based pharmacokinetic modelling prediction of the effects of dose adjustment in drug-drug interactions between levonorgestrel contraceptive implants and efavirenz-based ART

Background

HIV-positive women receiving efavirenz-based ART and levonorgestrel contraceptive implants are at risk of low levonorgestrel exposure and unintended pregnancy.

Objectives

To investigate clinically applicable dose-adjustment strategies to overcome the known drug-drug interaction (DDI) between levonorgestrel and efavirenz, using a physiologically based pharmacokinetic (PBPK) modelling-based approach.

Methods

A PBPK model was qualified against clinical data to predict levonorgestrel plasma concentrations when standard-dose (150 mg) levonorgestrel implants were administered alone (control group), as well as when standard-dose or increased-dose (300 mg) levonorgestrel implants were coadministered with either 600 or 400 mg of efavirenz.

Results

No difference was seen between in vivo clinical and PBPK-model-simulated levonorgestrel plasma concentrations (P > 0.05). Simulated levonorgestrel plasma concentrations were ∼50% lower at 48 weeks post-implant-placement in virtual individuals receiving standard-dose levonorgestrel with either 600 or 400 mg of efavirenz compared with the control group (efavirenz:control geometric mean ratio = 0.42 and 0.49, respectively). Conversely, increased-dose levonorgestrel in combination with either 600 or 400 mg of efavirenz was sufficient to restore levonorgestrel concentrations to levels similar to those observed in the 150 mg levonorgestrel control group 48 weeks post-implant-placement (efavirenz:control geometric mean ratio = 0.86 and 1.03, respectively).

Conclusions

These results suggest that the clinically significant DDI between efavirenz and levonorgestrel is likely to persist despite efavirenz dose reduction, whereas dose escalation of implantable levonorgestrel may represent a successful clinical strategy to circumvent efavirenz-levonorgestrel DDIs and will be of use to inform clinical trial design to assess coadministration of efavirenz and levonorgestrel implants.

In silico models in drug development: where we are

The use and utility of computational models in drug development has significantly grown in the last decades, fostered by the availability of high throughput datasets and new data analysis strategies. These in silico approaches are demonstrating their ability to generate reliable predictions as well as new knowledge on the mode of action of drugs and the mechanisms underlying their side effects, altogether helping to reduce the costs of drug development. The aim of this review is to provide a panorama of developments in the field in the last two years.

The emerging role of physiologically based pharmacokinetic modelling in solid drug nanoparticle translation

The use of solid drug nanoparticles (SDN) has become an established approach to improve drug delivery, supporting enhancement of oral absorption and long-acting administration strategies. A broad range of SDNs have been successfully utilised for multiple products and several development programmes are currently underway across different therapeutic areas. With some approaches, a large range of material space is available with diversity in physical characteristics, excipient choice and pharmacological behaviour. The selection of SDN lead candidates is a complex process including a broad range of in vitro and in vivo data, and a better understanding of how physical characteristics relate to performance is required. Physiologically-based pharmacokinetic (PBPK) modelling is based upon a comprehensive integration of experimental data into a mathematical description of drug distribution, allowing simulation of SDN pharmacokinetics that can be qualified in vivo prior to human prediction. This review aims to provide a description of how PBPK can find application into the development of SDN. Integration of predictive PBPK modelling into SDN development allows a better understanding of the SDN dose-response relationship, supporting a framework for rational optimisation while reducing the risk of failure in developing safe and effective nanomedicines.