Use of a physiologically based pharmacokinetic model to simulate drug-drug interactions between antineoplastic and antiretroviral drugs

Application of physiologically based pharmacokinetics modeling in the research of small-molecule targeted anti-cancer drugs

INTRODUCTION

Physiologically based pharmacokinetics (PBPK) models are increasingly used in the drug research and development, especially in anti-cancer drugs. Between 2001 and 2020, a total of 89 small-molecule targeted antitumor drugs were approved in China and the United States, some of which already included PBPK modeling in their application or approval packages. This article intended to review the prevalence and application of PBPK model in these drugs.

METHOD

Article search was performed in the PubMed to collect English research articles on small-molecule targeted anti-cancer drugs using PBPK modeling. The selected articles were classified into nine categorizes according to the application areas and further analyzed.

RESULT

From 2001 to 2020, more than 60% of small-molecule targeted anti-cancer drugs (54/89) were studied using PBPK model with a wide range of application. Ninety research articles were included, of which 48 involved enzyme-mediated drug-drug interaction (DDI). Of these retrieved articles, Simcyp, GastroPlus, and PK-Sim were the most widely model building platforms, which account for 63.8%, 15.2%, and 8.6%, respectively.

CONCLUSION

PBPK modeling is commonly and widely used to research small-molecule targeted anti-cancer drugs.

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.

Hodgkin Lymphoma in People Living with HIV

Simple Summary

Hodgkin lymphoma (HL) is a non-AIDS defining neoplasm, but people living with HIV (PLWH) have between a 5- and 26-fold higher risk of developing it than the general population. Epstein-Barr virus is present in almost all HIV-related HL cases, and plays an important role in its etiopathogenesis. Despite the aggressive characteristics, the prognosis of HL affecting PLWH is similar to that of the general population if patients are treated following the same recommendations. Administration of cART concomitantly with chemotherapy is highly recommended. However, this combination may be challenging due to drug–drug interactions and overlapping toxicity. Thus, interdisciplinary collaboration between hemato-oncologists and HIV specialists is crucial for the optimal treatment of both lymphoma and HIV infection.

Abstract

Despite widespread use of combined antiretroviral therapy (cART) and increased life expectancy in people living with HIV (PLWH), HIV-related lymphomas (HRL) remain a leading cause of cancer morbidity and mortality for PLWH, even in patients optimally treated with cART. While the incidence of aggressive forms of non-Hodgkin lymphoma decreased after the advent of cART, incidence of Hodgkin lymphoma (HL) has increased among PLWH in recent decades. The coinfection of Epstein–Barr virus plays a crucial role in the pathogenesis of HL in the HIV setting. Currently, PLWH with HRL, including HL, are treated similarly to HIV-negative patients and, importantly, the prognosis of HL in PLWH is approaching that of the general population. In this regard, effective cART during chemotherapy is strongly recommended since it has been shown to improve survival rates in all lymphoma subtypes, including HL. As a consequence, interdisciplinary collaboration between HIV specialists and hemato-oncologists for the management of potential drug–drug interactions and overlapping toxicities between antiretroviral and antineoplastic drugs is crucial for the optimal treatment of PLWH with HL. In this article the authors review and update the epidemiological, clinical and biological aspects of HL presenting in PLWH with special emphasis on advances in prognosis and the factors that have contributed to it.

Population pharmacokinetic modelling to quantify the magnitude of drug-drug interactions between amlodipine and antiretroviral drugs

PURPOSE

Drug-drug interactions (DDIs) with antiretroviral drugs (ARVs) represent an important issue in elderly people living with HIV (PLWH). Amlodipine is a commonly prescribed antihypertensive drug metabolized by CYP3A4, thus predisposed to a risk of DDIs. Guidance on the management of DDIs is mostly based on theoretical considerations derived from coadministration with other CYP3A4 inhibitors. This study aimed at characterizing the magnitude of DDIs between amlodipine and ARV drugs in order to establish dosing recommendations.

METHODS

A population pharmacokinetic analysis was developed using non-linear mixed effect modelling (NONMEM) and included 163 amlodipine concentrations from 55 PLWH. Various structural and error models were compared to characterize optimally the concentration-time profile of amlodipine. Demographic and clinical characteristics as well as comedications were tested as potential influential covariates. Model-based simulations were performed to compare amlodipine exposure (i.e. area under the curve, AUC) between coadministered ARV drugs.

RESULTS

Amlodipine concentration-time profile was best described using a one-compartment model with first-order absorption and a lag-time. Amlodipine apparent clearance was influenced by both CYP3A4 inhibitors and efavirenz (CYP3A4 inducer). Model-based simulations revealed that amlodipine AUC increased by 96% when coadministered with CYP3A4 inhibitors, while efavirenz decreased drug exposure by 59%.

CONCLUSION

Coadministered ARV drugs significantly impact amlodipine disposition in PLWH. Clinicians should adjust amlodipine dosage accordingly, by halving the dosage in PLWH receiving ARV with inhibitory properties (mainly ritonavir-boosted darunavir), whereas they should double amlodipine doses when coadministering it with efavirenz, under appropriate monitoring of clinical response and tolerance.

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.

Influence of Uridine Diphosphate Glucuronosyltransferase Family 1 Member A1 and Solute Carrier Organic Anion Transporter Family 1 Member B1 Polymorphisms and Efavirenz on Bilirubin Disposition in Healthy Volunteers

Chronic administration of efavirenz is associated with decreased serum bilirubin levels, probably through induction of UGT1A1 We assessed the impact of efavirenz monotherapy and UGT1A1 phenotypes on total, conjugated, and unconjugated serum bilirubin levels in healthy volunteers. Healthy volunteers were enrolled into a clinical study designed to address efavirenz pharmacokinetics, drug interactions, and pharmacogenetics. Volunteers received multiple oral doses (600 mg/day for 17 days) of efavirenz. Serum bilirubin levels were obtained at study entry and 1 week after completion of the study. DNA genotyping was performed for UGT1A1 [*80 (C>T), *6 (G>A), *28 (TA₇), *36 (TA₅), and *37 (TA₈)] and for SLCO1B1 [*5 (521T>C) and *1b (388A>G] variants. Diplotype predicted phenotypes were classified as normal, intermediate, and slow metabolizers. Compared with bilirubin levels at screening, treatment with efavirenz significantly reduced total, conjugated, and unconjugated bilirubin. After stratification by UGT1A1 phenotypes, there was a significant decrease in total bilirubin among all phenotypes, conjugated bilirubin among intermediate metabolizers, and unconjugated bilirubin among normal and intermediate metabolizers. The data also show that UGT1A1 genotype predicts serum bilirubin levels at baseline, but this relationship is lost after efavirenz treatment. SLCO1B1 genotypes did not predict bilirubin levels at baseline or after efavirenz treatment. Our data suggest that efavirenz may alter bilirubin disposition mainly through induction of UGT1A1 metabolism and efflux through multidrug resistance-associated protein 2. SIGNIFICANCE STATEMENT: Efavirenz likely alters the pharmacokinetics of coadministered drugs, potentially causing lack of efficacy or increased adverse effects, as well as the disposition of endogenous compounds relevant in homeostasis through upregulation of UGT1A1 and multidrug resistance-associated protein 2. Measurement of unconjugated and conjugated bilirubin during new drug development may provide mechanistic understanding regarding enzyme and transporters modulated by the new drug.

A case of esophageal cancer with human immunodeficiency virus infection that progressed rapidly after neoadjuvant chemoradiotherapy

While antiretroviral therapy has improved mortality in patients with human immunodeficiency virus (HIV) infections, deaths caused by non-acquired immunodeficiency syndrome-defining malignancies are increasing. A woman in her 70s with HIV infection who was receiving antiretroviral therapy presented with dysphagia. She was diagnosed with esophageal cancer (cT3N2M0, stage III). She received neoadjuvant chemotherapy (cisplatin and 5-fluorouracil) and radiotherapy. During treatment, we continued administering antiretroviral therapy and prophylaxis for opportunistic infections, with due attention to side effects and drug-drug interactions. No severe adverse events occurred. The primary lesion and metastatic lymph nodes decreased in size after treatment; however, 1 month later, her cancer spread to other organs; thus, surgery was canceled. Her general condition rapidly worsened. She eventually died of cancer cachexia and aspiration pneumonia. No previous reports have mentioned the treatment plan and management of esophageal cancer in HIV-positive patients. This report presents a case of esophageal cancer with HIV infection that progressed rapidly after neoadjuvant chemoradiotherapy.

A Comprehensive Framework for Physiologically-Based Pharmacokinetic Modeling in Matlab

Physiologically-based pharmacokinetic (PBPK) models are useful tools to predict clinical scenarios for special populations for whom there are high hurdles to conduct clinical trials such as children or the elderly. However, the coding of a PBPK model in a mathematical programming language can be challenging. This tutorial illustrates how to build a whole-body PBPK model in Matlab to answer specific pharmacological questions involving drug disposition and magnitudes of drug-drug interactions in different patient populations.

Oral oncolytic and antiretroviral therapy administration: dose adjustments, drug interactions, and other considerations for clinical use

The rise in non-AIDS defining cancers (NADCs) is emerging as a leading cause of death for HIV and cancer patients. To address this, current literature and guidelines suggest the continuation of antiretroviral therapy (ART) with oral oncolytic agents to prevent adverse complications associated with HIV disease progression. However, such an approach has the potential for drug-drug interactions and adverse events for patients on such therapy. Further, recommendations on how to adjust these medications, when used concomitantly, are limited. As such, our purpose is to evaluate existing literature through such means as drug databases (e.g. Micromedex, Lexi-Comp, etc.) and package inserts along with PubMed/Medline, Embase, and Google Scholar databases to develop a reference tool for providers to utilize when there is a decision to treat a patient with ART and oral oncolytic agents concurrently. Our findings suggest that there are many drug interactions that should be taken into consideration with dual therapy. Metabolism is a key determinant of dose adjustment, and many oncolytic agents and ART agents must have their dose adjusted as such. Most notably, several tyrosine kinase inhibitors require dose increases when used with non-nucleoside reverse transcriptase inhibitors (NNRTIs) but must be decreased when used concomitantly with protease inhibitors (PIs) and cobicistat. Further findings suggest that certain agents should not be used together, which include, but are not limited to, such combinations as bosutinib with NNRTIs, cobicistat, or PIs; idelalisib with maraviroc or PIs; neratinib with NNRTIs, cobicistat, or PIs; and venetoclax with NNRTIs. Overall, the most prominent oncolytic drug interactions were discovered when such agents were used concomitantly with PIs, cobicistat-boosted elvitegravir, or NNRTIs. Future studies are necessary to further evaluate the use of these agents together in disease therapy to generate absolute evidence of such findings. However, from the studies evaluated, much evidence exists to suggest that concomitant therapy is not without drug interactions. As such, clinical decisions regarding concomitant therapy should be evaluated in which the risk and benefit of dual therapy are assessed. Dose adjustments must be made accordingly and in consultation with both HIV and oncology clinicians and pharmacists to reduce the risk for adverse outcomes and disease progression for those with cancer and HIV/AIDS.