Physiologically-Based Pharmacokinetic Modeling to Support the Clinical Management of Drug-Drug Interactions With Bictegravir

Development and Validation of a High-Performance Liquid Chromatography Coupled With Ultraviolet Detection Method for Quantification of Bictegravir in Human Plasma

BACKGROUND

To establish a method for determining the bictegravir (BIC) concentration in human plasma using high-performance liquid chromatography coupled with ultraviolet detection.

METHODS

The analysis was performed on a CLC-octadecylsilane column (150 × 6.0 mm, 5 μm) using a mixture of phosphate buffer and acetonitrile (62:38, v/v) as the mobile phase at the flow rate of 1.4 mL/min. The column temperature was maintained at 40°C. Using triamcinolone acetonide as the internal standard, 100 μL of plasma sample was extracted by methyl tert-butyl ether, followed by evaporating under nitrogen stream, redissolving with 100 μL mobile phase, and injection of 20-40 μL of supernatant into the chromatographic system. Ultraviolet detection was performed at 260 nm, and the total run time for each sample was 14 minutes.

RESULTS

The method exhibited good linearity within the range from 0.10 to 10.0 mcg/mL (r = 0.9995, n = 5). The intraday and interday relative standard deviations for low-, medium-, and high-concentration quality control samples (0.20, 4.00, 8.00 mcg/mL) and the lower limit of quantification (0.10 mcg/mL) were 1.31%-6.20% (n = 10) and 1.18%-2.87% (n = 5), respectively. The intraday and interday accuracies were 100.53%-102.32% and 97.96%-103.84%, respectively. The extraction recovery rates ranged from 80.00% to 88.09% (n = 3). The stability tests showed that the BIC concentration changed by <15%.

CONCLUSIONS

This study successfully established a high-performance liquid chromatography coupled with ultraviolet detection method for determining plasma BIC concentrations. This method is simple, selective, sensitive, and accurate, making it suitable for clinical monitoring and pharmacokinetic studies of BIC.

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.

Development and application of a PBPK modeling strategy to support antimalarial drug development

As part of a collaboration between Medicines for Malaria Venture (MMV), Certara UK and Monash University, physiologically-based pharmacokinetic (PBPK) models were developed for 20 antimalarials, using data obtained from standardized in vitro assays and clinical studies within the literature. The models have been applied within antimalarial drug development at MMV for more than 5 years. During this time, a strategy for their impactful use has evolved. All models are described in the supplementary material and are available to researchers. Case studies are also presented, demonstrating real-world development and clinical applications, including the assessment of the drug-drug interaction liability between combination partners or with co-administered drugs. This work emphasizes the benefit of PBPK modeling for antimalarial drug development and decision making, and presents a strategy to integrate it into the research and development process. It also provides a repository of shared information to benefit the global health research community.

Drug-drug interactions between COVID-19 therapeutics and antiretroviral treatment: the evidence to date

INTRODUCTION

With new effective treatments for SARS-CoV-2, patient outcomes have greatly improved. However, new medications bring a risk of drug interactions with other medications. People living with HIV (PLWH) are at particular risk for these interactions due to heightened risk of immunosuppression, polypharmacy, and overlap in affected organs. It is critical to identify drug interactions are a significant barrier to care for PLWH. Establishing a better understanding of the pharmacologic relationships between COVID-19 therapies and antiretrovirals will improve patient-centered care in COVID-19.

AREAS COVERED

Potential drug-drug interactions between Human Immunodeficiency Virus (HIV) and COVID-19 treatments are detailed and reviewed here. The mechanisms seen in these interactions include alterations in metabolic enzymes, drug transporters, pharmacoenhancement, and organ toxicities. We also review the limitations and solutions that can be used to combat drug-drug interactions between these two disease states.

EXPERT OPINION

While current drug interactions are relatively mild between HIV and COVID-19 therapies, improvements in identifying these beforehand must take place as new therapies are approved. Antiretroviral therapy (ART) is essential in PLWH and must be maintained when treating COVID-19. As advancements in care occur, there is the possibility that newly approved drugs may have additional unknown interactions.

Drug-drug interactions that alter the exposure of glucuronidated drugs: Scope, UDP-glucuronosyltransferase (UGT) enzyme selectivity, mechanisms (inhibition and induction), and clinical significance

Drug-drug interactions (DDIs) arising from the perturbation of drug metabolising enzyme activities represent both a clinical problem and a potential economic loss for the pharmaceutical industry. DDIs involving glucuronidated drugs have historically attracted little attention and there is a perception that interactions are of minor clinical relevance. This review critically examines the scope and aetiology of DDIs that result in altered exposure of glucuronidated drugs. Interaction mechanisms, namely inhibition and induction of UDP-glucuronosyltransferase (UGT) enzymes and the potential interplay with drug transporters, are reviewed in detail, as is the clinical significance of known DDIs. Altered victim drug exposure arising from modulation of UGT enzyme activities is relatively common and, notably, the incidence and importance of UGT induction as a DDI mechanism is greater than generally believed. Numerous DDIs are clinically relevant, resulting in either loss of efficacy or an increased risk of adverse effects, necessitating dose individualisation. Several generalisations relating to the likelihood of DDIs can be drawn from the known substrate and inhibitor selectivities of UGT enzymes, highlighting the importance of comprehensive reaction phenotyping studies at an early stage of drug development. Further, rigorous assessment of the DDI liability of new chemical entities that undergo glucuronidation to a significant extent has been recommended recently by regulatory guidance. Although evidence-based approaches exist for the in vitro characterisation of UGT enzyme inhibition and induction, the availability of drugs considered appropriate for use as 'probe' substrates in clinical DDI studies is limited and this should be research priority.

A computational overview of integrase strand transfer inhibitors (INSTIs) against emerging and evolving drug-resistant HIV-1 integrase mutants

AIDS (Acquired immunodeficiency syndrome) is one of the chronic and potentially life-threatening epidemics across the world. Hitherto, the non-existence of definitive drugs that could completely cure the Human immunodeficiency virus (HIV) implies an urgent necessity for the discovery of novel anti-HIV agents. Since integration is the most crucial stage in retroviral replication, hindering it can inhibit overall viral transmission. The 5 FDA-approved integrase inhibitors were computationally investigated, especially owing to the rising multiple mutations against their susceptibility. This comparative study will open new possibilities to guide the rational design of novel lead compounds for antiretroviral therapies (ARTs), more specifically the structure-based design of novel Integrase strand transfer inhibitors (INSTIs) that may possess a better resistance profile than present drugs. Further, we have discussed potent anti-HIV natural compounds and their interactions as an alternative approach, recommending the urgent need to tap into the rich vein of indigenous knowledge for reverse pharmacology. Moreover, herein, we discuss existing evidence that might change in the near future.

MecDDI: Clarified Drug-Drug Interaction Mechanism Facilitating Rational Drug Use and Potential Drug-Drug Interaction Prediction

Drug-drug interactions (DDIs) are a major concern in clinical practice and have been recognized as one of the key threats to public health. To address such a critical threat, many studies have been conducted to clarify the mechanism underlying each DDI, based on which alternative therapeutic strategies are successfully proposed. Moreover, artificial intelligence-based models for predicting DDIs, especially multilabel classification models, are highly dependent on a reliable DDI data set with clear mechanistic information. These successes highlight the imminent necessity to have a platform providing mechanistic clarifications for a large number of existing DDIs. However, no such platform is available yet. In this study, a platform entitled "MecDDI" was therefore introduced to systematically clarify the mechanisms underlying the existing DDIs. This platform is unique in (a) clarifying the mechanisms underlying over 1,78,000 DDIs by explicit descriptions and graphic illustrations and (b) providing a systematic classification for all collected DDIs based on the clarified mechanisms. Due to the long-lasting threats of DDIs to public health, MecDDI could offer medical scientists a clear clarification of DDI mechanisms, support healthcare professionals to identify alternative therapeutics, and prepare data for algorithm scientists to predict new DDIs. MecDDI is now expected as an indispensable complement to the available pharmaceutical platforms and is freely accessible at: https://idrblab.org/mecddi/.

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery

Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.

Physiologically Based Pharmacokinetic Modelling to Investigate the Impact of the Cytokine Storm on CYP3A Drug Pharmacokinetics in COVID-19 Patients

Patients with coronavirus disease 2019 (COVID‐19) may experience a cytokine storm with elevated interleukin‐6 (IL‐6) levels in response to severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2). IL‐6 suppresses hepatic enzymes, including CYP3A; however, the effect on drug exposure and drug‐drug interaction magnitudes of the cytokine storm and resulting elevated IL‐6 levels have not been characterized in patients with COVID‐19. We used physiologically‐based pharmacokinetic (PBPK) modeling to simulate the effect of inflammation on the pharmacokinetics of CYP3A metabolized drugs. A PBPK model was developed for lopinavir boosted with ritonavir (LPV/r), using clinically observed data from people living with HIV (PLWH). The inhibition of CYPs by IL‐6 was implemented by a semimechanistic suppression model and verified against clinical data from patients with COVID‐19, treated with LPV/r. Subsequently, the verified model was used to simulate the effect of various clinically observed IL‐6 levels on the exposure of LPV/r and midazolam, a CYP3A model drug. Clinically observed LPV/r concentrations in PLWH and patients with COVID‐19 were predicted within the 95% confidence interval of the simulation results, demonstrating its predictive capability. Simulations indicated a twofold higher LPV exposure in patients with COVID‐19 compared with PLWH, whereas ritonavir exposure was predicted to be comparable. Varying IL‐6 levels under COVID‐19 had only a marginal effect on LPV/r pharmacokinetics according to our model. Simulations showed that a cytokine storm increased the exposure of the CYP3A paradigm substrate midazolam by 40%. Our simulations suggest that CYP3A metabolism is altered in patients with COVID‐19 having increased cytokine release. Caution is required when prescribing narrow therapeutic index drugs particularly in the presence of strong CYP3A inhibitors.