Use of a physiologically-based pharmacokinetic model to simulate artemether dose adjustment for overcoming the drug-drug interaction with efavirenz

New clinical application prospects of artemisinin and its derivatives: a scoping review

BACKGROUND

Recent research has suggested that artemisinin and its derivatives may have therapeutic effects on parasites, viruses, tumors, inflammation and skin diseases. This study aimed to review clinical research on artemisinin and its derivatives except anti-malaria and explore possible priority areas for future development.

METHODS

Relevant articles in English and Chinese published before 28 October 2021 were reviewed. All articles were retrieved and obtained from databases including WanFang, PubMed/MEDLINE, the Cochrane Library, China National Knowledge International, Embase, OpenGrey, the Grey Literature Report, Grey Horizon, and ClinicalTrials.gov. Studies were selected for final inclusion based on predefined criteria. Information was then extracted and analyzed by region, disease, outcome, and time to identify relevant knowledge gaps.

RESULTS

Seventy-seven studies on anti-parasitic (35), anti-tumor (16), anti-inflammatory (12), anti-viral (8), and dermatological treatments (7) focused on the safety and efficacy of artemisinin and its derivatives. The anti-parasitic clinical research developed rapidly, with a large number of trials, rapid clinical progress, and multiple research topics. In contrast, anti-viral research was limited and mainly stayed in phase I clinical trials (37.50%). Most of the studies were conducted in Asia (60%), followed by Africa (27%), Europe (8%), and the Americas (5%). Anti-parasite and anti-inflammatory research were mainly distributed in less developed continents such as Asia and Africa, while cutting-edge research such as anti-tumor has attracted more attention in Europe and the United States. At the safety level, 58 articles mentioned the adverse reactions of artemisinin and its derivatives, with only one study showing a Grade 3 adverse event, while the other studies did not show any related adverse reactions or required discontinuation. Most studies have discovered therapeutic effects of artemisinin or its derivatives on anti-parasitic (27), anti-tumor (9), anti-inflammatory (9) and dermatological treatment (6). However, the efficacy of artemisinin-based combination therapies (ACTs) for parasitic diseases (non-malaria) is still controversial.

CONCLUSIONS

Recent clinical studies suggest that artemisinin and its derivatives may be safe and effective candidates for anti-tumor, anti-parasitic, anti-inflammatory and dermatological drugs. More phase II/III clinical trials of artemisinin and its derivatives on antiviral effects are needed.

Assessment of cytochrome P450 3A4-mediated drug–drug interactions for ipatasertib using a fit-for-purpose physiologically based pharmacokinetic model

Purpose

Ipatasertib, a potent and highly selective small-molecule inhibitor of AKT, is currently under investigation for treatment of cancer. Ipatasertib is a substrate and a time-dependent inhibitor of CYP3A4. It exhibits non-linear pharmacokinetics at subclinical doses in the clinical dose escalation study. To assess the DDI risk of ipatasertib at the intended clinical dose of 400 mg with CYP3A4 inhibitors, inducers, and substrates, a fit-for-purpose physiologically based pharmacokinetic (PBPK) model of ipatasertib was developed.

Methods

The PBPK model was constructed in Simcyp using in silico, in vitro, and clinical data and was optimized and verified using clinical data.

Results

The PBPK model described non-linear pharmacokinetics of ipatasertib and captured the magnitude of the observed clinical DDIs. Following repeated doses of 400 mg ipatasertib once daily (QD), the PBPK model predicted a 3.3-fold increase of ipatasertib exposure with itraconazole; a 2–2.5-fold increase with moderate CYP3A4 inhibitors, erythromycin and diltiazem; and no change with a weak CYP3A4 inhibitor, fluvoxamine. Additionally, in the presence of strong or moderate CYP3A4 inducers, rifampicin and efavirenz, ipatasertib exposures were predicted to decrease by 86% and 74%, respectively. As a perpetrator, the model predicted that ipatasertib (400 mg) caused a 1.7-fold increase in midazolam exposure.

Conclusion

This study demonstrates the value of using a fit-for-purpose PBPK model to assess the clinical DDIs for ipatasertib and to provide dosing strategies for the concurrent use of other CYP3A4 perpetrators or victims.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00280-022-04434-2.

Prediction of dolutegravir pharmacokinetics and dose optimization in neonates via physiologically based pharmacokinetic (PBPK) modelling

BACKGROUND

Only a few antiretroviral drugs (ARVs) are recommended for use during the neonatal period and there is a need for more to be approved to increase treatment and prophylaxis strategies. Dolutegravir, a selective integrase inhibitor, has potential for treatment of HIV infection and prophylaxis of transmission in neonates.

OBJECTIVES

To model the pharmacokinetics of dolutegravir in neonates and to simulate a theoretical optimal dosing regimen.

METHODS

The physiologically based pharmacokinetic (PBPK) model was built incorporating the age-related changes observed in neonates. Virtual neonates between 0 and 28 days were simulated. The model was validated against observed clinical data for raltegravir and midazolam in neonates, prior to the prediction of dolutegravir pharmacokinetics.

RESULTS

Both raltegravir and midazolam passed the criteria for model qualification, with simulated data within 1.8-fold of clinical data. The qualified model predicted the pharmacokinetics for several multidose regimens of dolutegravir. Regimen 6 involved 5 mg doses with a 48 h interval from Day 1-20, increasing to 5 mg once daily on Week 3, yielding AUC and Ctrough values of 37.2 mg·h/L and 1.3 mg/L, respectively. These exposures are consistent with those observed in paediatric patients receiving dolutegravir.

CONCLUSIONS

Dolutegravir pharmacokinetics were successfully simulated in the neonatal PBPK model. The predictions suggest that during the first 3 weeks of life a 5 mg dose administered every 48 h may achieve plasma exposures needed for therapy and prophylaxis.

Model-Informed Drug Development for Anti-Infectives: State of the Art and Future

Model-informed drug development (MIDD) has a long and rich history in infectious diseases. This review describes foundational principles of translational anti-infective pharmacology, including choice of appropriate measures of exposure and pharmacodynamic (PD) measures, patient subpopulations, and drug-drug interactions. Examples are presented for state-of-the-art, empiric, mechanistic, interdisciplinary, and real-world evidence MIDD applications in the development of antibacterials (review of minimum inhibitory concentration-based models, mechanism-based pharmacokinetic/PD (PK/PD) models, PK/PD models of resistance, and immune response), antifungals, antivirals, drugs for the treatment of global health infectious diseases, and medical countermeasures. The degree of adoption of MIDD practices across the infectious diseases field is also summarized. The future application of MIDD in infectious diseases will progress along two planes; "depth" and "breadth" of MIDD methods. "MIDD depth" refers to deeper incorporation of the specific pathogen biology and intrinsic and acquired-resistance mechanisms; host factors, such as immunologic response and infection site, to enable deeper interrogation of pharmacological impact on pathogen clearance; clinical outcome and emergence of resistance from a pathogen; and patient and population perspective. In particular, improved early assessment of the emergence of resistance potential will become a greater focus in MIDD, as this is poorly mitigated by current development approaches. "MIDD breadth" refers to greater adoption of model-centered approaches to anti-infective development. Specifically, this means how various MIDD approaches and translational tools can be integrated or connected in a systematic way that supports decision making by key stakeholders (sponsors, regulators, and payers) across the entire development pathway.

Physiologically-Based Pharmacokinetic Modeling for Optimal Dosage Prediction of Quinine Coadministered With Ritonavir-Boosted Lopinavir

The coformulated lopinavir/ritonavir significantly reduces quinine concentration in healthy volunteers due to potential drug-drug interactions (DDIs). However, DDI information in malaria and HIV coinfected patients are lacking. The objective of the study was to apply physiologically-based pharmacokinetic (PBPK) modeling to predict optimal dosage regimens of quinine when coadministered with lopinavir/ritonavir in malaria and HIV coinfected patients with different conditions. The developed model was validated against literature. Model verification was evaluated using the accepted method. The verified PBPK models successfully predicted unbound quinine disposition when coadministered with lopinavir/ritonavir in coinfected patients with different conditions. Suitable dose adjustments to counteract with the DDIs have identified in patients with various situations (i.e., a 7-day course at 1,800 mg t.i.d. in patients with malaria with HIV infection, 648 mg b.i.d. in chronic renal failure, 648 mg t.i.d. in hepatic insufficiency except for severe hepatic insufficiency (324 mg b.i.d.), and 648 mg t.i.d. in CYP3A4 polymorphism).

Using mechanistic physiologically-based pharmacokinetic models to assess prenatal drug exposure: Thalidomide versus efavirenz as case studies

Maternofoetal physiologically-based pharmacokinetic models integrating multi-compartmental maternal and foetal units were developed using Simbiology® to estimate prenatal drug exposure. Processes governing drug disposition were described using differential equations with key system and drug-specific parameters. Transplacental drug transfer was modelled as bidirectional passive diffusion and benchmarked against those for thalidomide as a control. Model-predictions for pharmacokinetic parameters during pregnancy were within acceptable ranges for qualification (two-fold difference of clinically-observed values). Predicted foetal exposure to thalidomide was higher than efavirenz, with median (range) foetal-to-maternal plasma ratios of 4.55 (3.06–9.57) for 400 mg thalidomide versus 0.89 (0.73–1.05) for 400 mg efavirenz at third trimester. Model-predictions indicated foetal exposure consistently above 300% of maternal plasma concentration for thalidomide throughout pregnancy, while exposure to efavirenz increased from under 20% at second trimester to above 100% at third trimester. Further qualification of this approach as a tool in evaluating drug exposure and safety during pregnancy is warranted.

Effect of Pregnancy on the Pharmacokinetic Interaction between Efavirenz and Lumefantrine in HIV-Malaria Coinfection

Artemether-lumefantrine is often coadministered with efavirenz-based antiretroviral therapy for malaria treatment in HIV-infected women during pregnancy. Previous studies showed changes in lumefantrine pharmacokinetics due to interaction with efavirenz in nonpregnant adults. The influence of pregnancy on this interaction has not been reported. This pharmacokinetic study involved 35 pregnant and 34 nonpregnant HIV-malaria-coinfected women receiving efavirenz-based antiretroviral therapy and was conducted in four health facilities in Nigeria. Participants received a 3-day standard regimen of artemether-lumefantrine for malaria treatment, and intensive pharmacokinetic sampling was conducted from 0.5 to 96 h after the last dose. Plasma efavirenz, lumefantrine, and desbutyl-lumefantrine were quantified using validated assays, and pharmacokinetic parameters were derived using noncompartmental analysis. The median middose plasma concentrations of efavirenz were significantly lower in pregnant women (n = 32) than in nonpregnant women (n = 32) at 1,820 ng/ml (interquartile range, 1,300 to 2,610 ng/ml) versus 2,760 ng/ml (interquartile range, 2,020 to 5,640 ng/ml), respectively (P = 0.006). The lumefantrine area under the concentration-time curve from 0 to 96 h was significantly higher in pregnant women (n = 27) at 155,832 ng · h/ml (interquartile range, 102,400 to 214,011 ng · h/ml) than nonpregnant women at 90,594 ng · h/ml (interquartile range, 58,869 to 149,775 ng · h/ml) (P = 0.03). A similar trend was observed for the lumefantrine concentration at 12 h after the last dose of lumefantrine, which was 2,870 ng/ml (interquartile range, 2,180 to 4,880 ng/ml) versus 2,080 ng/ml (interquartile range, 1,190 to 2,970 ng/ml) in pregnant and nonpregnant women, respectively (P = 0.02). The lumefantrine-to-desbutyl-lumefantrine ratio also tended to be lower in pregnant women than in nonpregnant women (P = 0.076). Overall, pregnancy tempered the extent of efavirenz-lumefantrine interactions, resulting in increased lumefantrine exposure. However, any consideration of dosage adjustment for artemether-lumefantrine to enhance exposure in this population needs to be based on data from a prospective study with safety and efficacy endpoints.

Physiologically-based pharmacokinetic modelling of infant exposure to efavirenz through breastfeeding

Background: Very little is known about the level of infant exposure to many drugs commonly used during breastfeeding. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for predicting infant exposure to maternal efavirenz through breastmilk. Methods: A breastfeeding PBPK model combining whole-body maternal and infant sub-models was constructed from drug-specific and system parameters affecting drug disposition using mathematical descriptions. The model was validated against published data on the pharmacokinetics of efavirenz in nursing mother-infant pairs. Further simulations were conducted to assess exposure in the context of the 400 mg reduced dose of efavirenz as well as best- and worse-case scenarios. Results: The model adequately described efavirenz pharmacokinetics, with over 80% of observed data points (203 matched breast milk and plasma pairs) within the predictive interval. All parameters were within 2-fold difference of clinical data. Median (range) predicted versus observed breast milk AUC0-24, Cmax and Cmin at the standard 600 mg dose were 75.0 (18.5-324) versus 68.5 (26.3-257) µg.hr/mL, 4.56 (1.17-16.0) versus 5.39 (1.43-18.4) µg/mL, and 2.11 (0.38-12.3) versus 1.68 (0.316-9.57) µg/mL, respectively. Predicted plasma AUC0-24, Cmax and Cmin at 400 mg reduced dose were similar to clinical data from non-breastfeeding adults. Model-predicted infant plasma concentrations were similar to clinical data, 0.15 (0.026–0.78) μg/mL at the 400 mg maternal dose in pooled analysis, approximately 25% lower than simulated exposure at 600 mg. The maximum exposure index was observed in the youngest infants, 5.9% (2.2-20) at 400 mg and 8.7% (3.2-29) at 600 mg. Thirteen and 36% of 10 days-1 month old infants were predicted to have exposure index above the 10% recommended threshold at 400 mg and 600 mg maternal dose, respectively. Conclusions: This application of PBPK modelling opens up opportunities for expanding our understanding of infant exposure to maternal drugs through breastfeeding.

Human cytochrome P450 2B6 genetic variability in Botswana: a case of haplotype diversity and convergent phenotypes

Identification of inter-individual variability for drug metabolism through cytochrome P450 2B6 (CYP2B6) enzyme is important for understanding the differences in clinical responses to malaria and HIV. This study evaluates the distribution of CYP2B6 alleles, haplotypes and inferred metabolic phenotypes among subjects with different ethnicity in Botswana. A total of 570 subjects were analyzed for CYP2B6 polymorphisms at position 516 G > T (rs3745274), 785 A > G (rs2279343) and 983 T > C (rs28399499). Samples were collected in three districts of Botswana where the population belongs to Bantu (Serowe/Palapye and Chobe) and San-related (Ghanzi) ethnicity. The three districts showed different haplotype composition according to the ethnic background but similar metabolic inferred phenotypes, with 59.12%, 34.56%, 2.10% and 4.21% of the subjects having, respectively, an extensive, intermediate, slow and rapid metabolic profile. The results hint at the possibility of a convergent adaptation of detoxifying metabolic phenotypes despite a different haplotype structure due to the different genetic background. The main implication is that, while there is substantial homogeneity of metabolic inferred phenotypes among the country, the response to drugs metabolized via CYP2B6 could be individually associated to an increased risk of treatment failure and toxicity. These are important facts since Botswana is facing malaria elimination and a very high HIV prevalence.

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

BACKGROUND

Antiretroviral drugs are among the therapeutic agents with the highest potential for drug-drug interactions (DDIs). In the absence of clinical data, DDIs are mainly predicted based on preclinical data and knowledge of the disposition of individual drugs. Predictions can be challenging, especially when antiretroviral drugs induce and inhibit multiple cytochrome P450 (CYP) isoenzymes simultaneously.

METHODS

This study predicted the magnitude of the DDI between efavirenz, an inducer of CYP3A4 and inhibitor of CYP2C8, and dual CYP3A4/CYP2C8 substrates (repaglinide, montelukast, pioglitazone, paclitaxel) using a physiologically based pharmacokinetic (PBPK) modeling approach integrating concurrent effects on CYPs. In vitro data describing the physicochemical properties, absorption, distribution, metabolism, and elimination of efavirenz and CYP3A4/CYP2C8 substrates as well as the CYP-inducing and -inhibitory potential of efavirenz were obtained from published literature. The data were integrated in a PBPK model developed using mathematical descriptions of molecular, physiological, and anatomical processes defining pharmacokinetics. Plasma drug-concentration profiles were simulated at steady state in virtual individuals for each drug given alone or in combination with efavirenz. The simulated pharmacokinetic parameters of drugs given alone were compared against existing clinical data. The effect of efavirenz on CYP was compared with published DDI data.

RESULTS

The predictions indicate that the overall effect of efavirenz on dual CYP3A4/CYP2C8 substrates is induction of metabolism. The magnitude of induction tends to be less pronounced for dual CYP3A4/CYP2C8 substrates with predominant CYP2C8 metabolism.

CONCLUSION

PBPK modeling constitutes a useful mechanistic approach for the quantitative prediction of DDI involving simultaneous inducing or inhibitory effects on multiple CYPs as often encountered with antiretroviral drugs.

Drug interactions and the role of pharmacokinetic trials in guiding choices in first-line HIV therapy in low-income and middle-income countries

PURPOSE OF REVIEW

Low- and middle-income countries (LMICs) face specific challenges in the treatment of people living with HIV. Drug-drug interactions (DDIs) involving antiretrovirals (ARVs) are prevalent in all settings and have considerable potential to cause clinical harm to patients via toxicity or reduced efficacy of treatment. Differing comorbidities, endemic infections and traditional medicines may complicate ARV therapy (ART) in LMICs, which usually takes a public health approach in these settings, with fewer alternative regimens available. This review discusses the issues surrounding pharmacokinetic DDI studies and their application to ART in LMICs, with particular reference to first-line ART regimens.

RECENT FINDINGS

Pharmacokinetic studies with clinical endpoints are the gold standard for informing management of DDIs; however, data relevant to LMICs are sparse and of low quality. There is significant potential for clinically relevant DDIs between ARVs and antimalarials, antimycobacterials and drugs used in the treatment of neglected tropical diseases.

SUMMARY

Many pharmacokinetic studies are difficult to interpret in LMICs due to differences in patient factors including weight, disease state and genetic polymorphisms. DDI studies relevant to LMICs may also be lacking due to the neglected nature of relevant comorbidities. The ARVs currently available as first-line ART in LMICs are among those with highest propensity for DDIs.

Development of a paediatric physiologically based pharmacokinetic model to assess the impact of drug-drug interactions in tuberculosis co-infected malaria subjects: A case study with artemether-lumefantrine and the CYP3A4-inducer rifampicin

The fixed dosed combination of artemether and lumefantrine (AL) is widely used for the treatment of malaria in adults and children in sub-Sahara Africa, with lumefantrine day 7 concentrations being widely used as a marker for clinical efficacy. Both are substrates for CYP3A4 and susceptible to drug-drug interactions (DDIs); indeed, knowledge of the impact of these factors is currently sparse in paediatric population groups. Confounding malaria treatment is the co-infection of patients with tuberculosis. The concomitant treatment of AL with tuberculosis chemotherapy, which includes the CYP3A4 inducer rifampicin, increases the risk of parasite recrudescence and malaria treatment failure. This study developed a population-based PBPK model for AL in adults capable of predicting the pharmacokinetics of AL under non-DDI and DDI conditions, as well as predicting AL pharmacokinetics in paediatrics of 2-12years of age. The validated model was utilised to assess the concomitant treatment of rifampicin and lumefantrine under standard body-weight based treatment regimens for 2-5year olds, and demonstrated that no subjects attained the target day 7 concentration (C_d7) of 280ng/mL, highlighting the importance of this DDI and the potential risk of malaria-TB based DDIs. An adapted 7-day treatment regimen was simulated and resulted in 63% and 74.5% of subjects attaining the target C_d7 for 1-tablet and 2-tablet regimens respectively.

Development of an evidence evaluation and synthesis system for drug-drug interactions, and its application to a systematic review of HIV and malaria co-infection

BACKGROUND

In all settings, there are challenges associated with safely treating patients with multimorbidity and polypharmacy. The need to characterise, understand and limit harms resulting from medication use is therefore increasingly important. Drug-drug interactions (DDIs) are prevalent in patients taking antiretrovirals (ARVs) and if unmanaged, may pose considerable risk to treatment outcome. One of the biggest challenges in preventing DDIs is the substantial gap between theory and clinical practice. There are no robust methods published for formally assessing quality of evidence relating to DDIs, despite the diverse sources of information. We defined a transparent, structured process for developing evidence quality summaries in order to guide therapeutic decision making. This was applied to a systematic review of DDI data with considerable public health significance: HIV and malaria.

METHODS AND FINDINGS

This was a systematic review of DDI data between antiretrovirals and drugs used in prophylaxis and treatment of malaria. The data comprised all original research in humans that evaluated pharmacokinetic data and/or related adverse events when antiretroviral agents were combined with antimalarial agents, including healthy volunteers, patients with HIV and/or malaria, observational studies, and case reports. The data synthesis included 36 articles and conference presentations published via PubMed and conference websites/abstract books between 1987-August 2016. There is significant risk of DDIs between HIV protease inhibitors, or NNRTIs and artemesinin-containing antimalarial regimens. For many antiretrovirals, DDI studies with antimalarials were lacking, and the majority were of moderate to very low quality. Quality of evidence and strength of recommendation categories were defined and developed specifically for recommendations concerning DDIs.

CONCLUSIONS

There is significant potential for DDIs between antiretrovirals and antimalarials. The application of quality of evidence and strength of recommendation criteria to DDI data is feasible, and allows the assessment of DDIs to be robust, consistent, transparent and evidence-based.

Efavirenz Is Predicted To Accumulate in Brain Tissue: an In Silico, In Vitro, and In Vivo Investigation

Adequate concentrations of efavirenz in the central nervous system (CNS) are necessary to suppress viral replication, but high concentrations may increase the likelihood of CNS adverse drug reactions. The aim of this investigation was to evaluate the efavirenz distribution in the cerebrospinal fluid (CSF) and the brain by using a physiologically based pharmacokinetic (PBPK) simulation for comparison with rodent and human data. The efavirenz CNS distribution was calculated using a permeability-limited model on a virtual cohort of 100 patients receiving efavirenz (600 mg once daily). Simulation data were then compared with human data from the literature and with rodent data. Wistar rats were administered efavirenz (10 mg kg of body weight^-1) once daily over 5 weeks. Plasma and brain tissue were collected for analysis via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The median maximum concentrations of drug (C_max) were predicted to be 3,184 ng ml^-1 (interquartile range [IQR], 2,219 to 4,851 ng ml^-1), 49.9 ng ml^-1 (IQR, 36.6 to 69.7 ng ml^-1), and 50,343 ng ml^-1 (IQR, 38,351 to 65,799 ng ml^-1) in plasma, CSF, and brain tissue, respectively, giving a tissue-to-plasma ratio of 15.8. Following 5 weeks of oral dosing of efavirenz (10 mg kg^-1), the median plasma and brain tissue concentrations in rats were 69.7 ng ml^-1 (IQR, 44.9 to 130.6 ng ml^-1) and 702.9 ng ml^-1 (IQR, 475.5 to 1,018.0 ng ml^-1), respectively, and the median tissue-to-plasma ratio was 9.5 (IQR, 7.0 to 10.9). Although it is useful, measurement of CSF concentrations may give an underestimation of the penetration of antiretrovirals into the brain. The limitations associated with obtaining tissue biopsy specimens and paired plasma and CSF samples from patients make PBPK modeling an attractive tool for probing drug distribution.

Accelerated oral nanomedicine discovery from miniaturized screening to clinical production exemplified by paediatric HIV nanotherapies

Considerable scope exists to vary the physical and chemical properties of nanoparticles, with subsequent impact on biological interactions; however, no accelerated process to access large nanoparticle material space is currently available, hampering the development of new nanomedicines. In particular, no clinically available nanotherapies exist for HIV populations and conventional paediatric HIV medicines are poorly available; one current paediatric formulation utilizes high ethanol concentrations to solubilize lopinavir, a poorly soluble antiretroviral. Here we apply accelerated nanomedicine discovery to generate a potential aqueous paediatric HIV nanotherapy, with clinical translation and regulatory approval for human evaluation. Our rapid small-scale screening approach yields large libraries of solid drug nanoparticles (160 individual components) targeting oral dose. Screening uses 1 mg of drug compound per library member and iterative pharmacological and chemical evaluation establishes potential candidates for progression through to clinical manufacture. The wide applicability of our strategy has implications for multiple therapy development programmes.

Towards a rational design of solid drug nanoparticles with optimised pharmacological properties

Solid drug nanoparticles (SDNs) are a nanotechnology with favourable characteristics to enhance drug delivery and improve the treatment of several diseases, showing benefit for improved oral bioavailability and injectable long‐acting medicines. The physicochemical properties and composition of nanoformulations can influence the absorption, distribution, and elimination of nanoparticles; consequently, the development of nanoparticles for drug delivery should consider the potential role of nanoparticle characteristics in the definition of pharmacokinetics. The aim of this study was to investigate the pharmacological behaviour of efavirenz SDNs and the identification of optimal nanoparticle properties and composition. Seventy‐seven efavirenz SDNs were included in the analysis. Cellular accumulation was evaluated in HepG2 (hepatic) and Caco‐2 (intestinal), CEM (lymphocyte), THP1 (monocyte), and A‐THP1 (macrophage) cell lines. Apparent intestinal permeability (P_app) was measured using a monolayer of Caco‐2 cells. The P_app values were used to evaluate the potential benefit on pharmacokinetics using a physiologically based pharmacokinetic model. The generated SDNs had an enhanced intestinal permeability and accumulation in different cell lines compared to the traditional formulation of efavirenz. Nanoparticle size and excipient choice influenced efavirenz apparent permeability and cellular accumulation, and this appeared to be cell line dependent. These findings represent a valuable platform for the design of SDNs, giving an empirical background for the selection of optimal nanoparticle characteristics and composition. Understanding how nanoparticle components and physicochemical properties influence pharmacological patterns will enable the rational design of SDNs with desirable pharmacokinetics.

Towards a Best Practice Approach in PBPK Modeling: Case Example of Developing a Unified Efavirenz Model Accounting for Induction of CYPs 3A4 and 2B6

In this study, we present efavirenz physiologically based pharmacokinetic (PBPK) model development as an example of our best practice approach that uses a stepwise approach to verify the different components of the model. First, a PBPK model for efavirenz incorporating in vitro and clinical pharmacokinetic (PK) data was developed to predict exposure following multiple dosing (600 mg q.d.). Alfentanil i.v. and p.o. drug‐drug interaction (DDI) studies were utilized to evaluate and refine the CYP3A4 induction component in the liver and gut. Next, independent DDI studies with substrates of CYP3A4 (maraviroc, atazanavir, and clarithromycin) and CYP2B6 (bupropion) verified the induction components of the model (area under the curve [AUC] ratios within 1.0–1.7‐fold of observed). Finally, the model was refined to incorporate the fractional contribution of enzymes, including CYP2B6, propagating autoinduction into the model (Racc 1.7 vs. 1.7 observed). This validated mechanistic model can now be applied in clinical pharmacology studies to prospectively assess both the victim and perpetrator DDI potential of efavirenz.

Physiologically Based Pharmacokinetic Modelling to Inform Development of Intramuscular Long-Acting Nanoformulations for HIV

BACKGROUND AND OBJECTIVES

Antiretrovirals are currently used for the treatment and prevention of HIV infection. However, poor adherence and low tolerability of some existing oral formulations can hinder their efficacy. Long-acting (LA) injectable nanoformulations could help address these complications by simplifying antiretroviral administration. The aim of this study is to inform the optimisation of intramuscular LA formulations for eight antiretrovirals through physiologically based pharmacokinetic (PBPK) modelling.

METHODS

A whole-body PBPK model was constructed using mathematical descriptions of molecular, physiological and anatomical processes defining pharmacokinetics. These models were validated against available clinical data and subsequently used to predict the pharmacokinetics of injectable LA formulations

RESULTS

The predictions suggest that monthly intramuscular injections are possible for dolutegravir, efavirenz, emtricitabine, raltegravir, rilpivirine and tenofovir provided that technological challenges to control their release rate can be addressed.

CONCLUSIONS

These data may help inform the target product profiles for LA antiretroviral reformulation strategies.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

Physiologically based pharmacokinetic models for the optimization of antiretroviral therapy: recent progress and future perspective

Anti-HIV therapy is characterized by the chronic administration of antiretrovirals (ARVs), and consequently, several problems can arise during the management of HIV-positive patients. ARV disposition can be simulated by combining system data describing a population of patients and in vitro drug data through physiologically based pharmacokinetic (PBPK) models, which mathematically describe absorption, distribution, metabolism and elimination. PBPK modeling can find application in the investigation of clinically relevant scenarios, while providing the opportunity for a better understanding of the mechanisms regulating drug distribution. In this review, we have analyzed the most recent applications of PBPK models for ARVs and highlighted some of the most interesting areas of use, such as drug–drug interaction, pharmacogenetics, factors regulating absorption and tissue penetration, as well as therapy optimization in special populations. The application of the PBPK modeling approach might not be limited to the investigation of hypothetical clinical issues, but could be used to inform future prospective clinical trials.

Computational pharmacokinetics at a crossroads

This first special issue of In Silico Pharmacology focuses on computational pharmacokinetics since they are an important part of integrated applications in computational pharmacology. The important topics of model structure, model parameterization, improved organ description, and modeling of drug-drug interactions are covered. They are actually at the crossroads between several emerging disciplines which will shape the future of therapeutic treatments and public health.