Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development

Cardio-oncology Drug Interactions: A Primer for Clinicians on Select Cardiotoxic Oncologic Therapies

Cardio-oncology is an emerging multidisciplinary field intended to mitigate and manage cardiovascular side effects and risks associated with cancer therapies. Clinician awareness of drug interaction management among cancer treatments, cardiovascular medications, and supportive care agents is important for optimizing efficacy and safety. Historically, chemotherapies have been associated with pharmacodynamic interactions with few, but important, pharmacokinetic interactions. The advent of oral targeted inhibitors has introduced more complex pharmacokinetic interactions, especially via cytochrome P450 pathways. Given the accelerated development of oncology therapies, clinicians need to be familiar with reviewing multiple sources for interaction information as well as adjusting and monitoring regimens when contending with drug interaction challenges.

Towards the understanding of the IVPT results variability-Development, verification and validation of the PBPK model of caffeine in vitro human skin permeation

In the context of evaluating the safety and efficacy of dermal products, pharmacokinetic (PK) studies face considerable challenges, particularly concerning topically applied formulations. This underscores the necessity for alternative methods, such as in vitro permeation tests (IVPT) and physiologically based pharmacokinetic (PBPK) modelling, to better understand the dermal pharmacokinetics of a product. The purpose of this study was to modify, verify, and validate the PBPK model of caffeine permeation through human skin previously developed by Patel et al. (2022), and compare simulation results with experimental data from IVPT studies. Moreover, the study aimed to analyse the IVPT data variability and explore the potential of using the PBPK model to understand the influence of biological and drug-related factors on the IVPT results. In total, eight manuscripts describing nine experiments were included. The overall shapes of the permeation curves were considered acceptable based on visual checks for all analysed experiments. Five out of nine experiments met the predefined standard 2-fold difference criterion for comparison of the cumulative amount of caffeine in the receptor solution.. Our investigation highlights challenges in validating PBPK models for IVPT experiments, as the quality and consistency of experimental results pose significant hurdles. Despite access to data on caffeine permeation in scientific literature, reliable model validation is currently infeasible. Inter-laboratory variation suggests that alternative validation methods may be needed. Further studies should focus on issues with other compounds, especially lipophilic ones.

Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery

Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial intelligence (AI) and virtual screening to facilitate the rational design of ionizable lipids by predicting two key properties of LNPs, apparent pKa and mRNA delivery efficiency. Nearly 20 million ionizable lipids were evaluated through two iterations of AI-driven generation and screening, yielding three and six new molecules, respectively. In mouse test validation, one lipid from the initial iteration, featuring a benzene ring, demonstrated performance comparable to the control DLin-MC3-DMA (MC3). Notably, all six lipids from the second iteration equaled or outperformed MC3, with one exhibiting efficacy akin to a superior control lipid SM-102. Furthermore, the AI model is interpretable in structure-activity relationships.

Evaluation of BCRP-Related DDIs Between Methotrexate and Cyclosporin A Using Physiologically Based Pharmacokinetic Modelling

BACKGROUND AND OBJECTIVE

This study provides a physiologically based pharmacokinetic (PBPK) model-based analysis of the potential drug-drug interaction (DDI) between cyclosporin A (CsA), a breast cancer resistance protein transporter (BCRP) inhibitor, and methotrexate (MTX), a putative BCRP substrate.

METHODS

PBPK models for CsA and MTX were built using open-source tools and published data for both model building and for model verification and validation. The MTX and CsA PBPK models were evaluated for their application in simulating BCRP-related DDIs. A qualification of an introduced empirical uniform in vitro scaling factor of Ki values for transporter inhibition by CsA was conducted by using a previously developed model of rosuvastatin (sensitive index BCRP substrate), and assessing if corresponding DDI ratios were well captured.

RESULTS

Within the simulated DDI scenarios for MTX in the presence of CsA, the developed models could capture the observed changes in PK parameters as changes in the area under the curve ratios (area under the curve during DDI/area under the curve control) of 1.30 versus 1.31 observed and the DDI peak plasma concentration ratios (peak plasma concentration during DDI/peak plasma concentration control) of 1.07 versus 1.28 observed. The originally reported in vitro Ki values of CsA were scaled with the uniform qualified scaling factor for their use in the in vivo DDI simulations to correct for the low intracellular unbound fraction of the CsA effector concentration. The resulting predicted versus observed ratios of peak plasma concentration and area under the curve DDI ratios with MTX were 0.82 and 0.99, respectively, indicating adequate model accuracy and choice of a scaling factor to capture the observed DDI.

CONCLUSIONS

All models have been comprehensively documented and made publicly available as tools to support the drug development and clinical research community and further community-driven model development.

Establishment of Biopredictive Dissolution and Bioequivalence Safe Space Using the Physiologically Based Biopharmaceutics Modeling for Tacrolimus Extended-Release Capsules

A slight variation in in vivo exposure for tacrolimus extended-release (ER) capsules, which have a narrow therapeutic index (NTI), significantly affects the pharmacodynamics of the drug. Generic drug bioequivalence (BE) standards are stricter, necessitating accurate assessment of the rate and extent of drug release. Therefore, an in vitro dissolution method with high in vivo predictive power is crucial for developing generic drugs. In this study, physiologically based biopharmaceutics modeling (PBBM) for 5 mg tacrolimus ER capsules was developed and validated. The reference and non-BE test formulations were assessed using the Flow-Through Cell apparatus (USP IV) with biorelevant media to establish a biopredictive dissolution method. Using PBBM, virtual bioequivalence trials with virtual batches were conducted to propose a BE safe space. These criteria can identify formulations that pass the internal quality control test but are likely non-BE. This study highlights the benefits of developing biopredictive dissolution methods that are based on biorelevant dissolution. The PBBM, constructed by integrating various drug parameters, combined with the developed biopredictive dissolution methods, is a convenient approach for BE evaluation of NTI drugs and a practical tool for developing new drugs.

Model-informed drug discovery and development approaches to inform clinical trial design and regulatory decisions: A primer for the MENA region

Model-Informed Drug Discovery and Development (MID3) represents a transformative approach in pharmaceutical research, integrating quantitative models to inform and optimize decision-making throughout the drug development process. This review explores the current applications, challenges, and future prospects of MID3 within the Middle East and North Africa (MENA) region. By leveraging local data and advanced computational techniques, MID3 has the potential to significantly enhance the efficiency and success rates of drug development tailored to regional health priorities. We discussed successful case studies of applying MID3 at different phases of drug development and clinical trials. Furthermore, we emphasized the critical need for MENA countries to embrace MID3 by investing in workforce training, aligning regulatory frameworks, and fostering collaborative research initiatives. This call to action underscores the importance of a robust MID3 ecosystem, urging policymakers, academic institutions, and industry stakeholders to prioritize and support its integration into the MENA region's healthcare.

Clinical and Physiologically Based Pharmacokinetic Model Evaluations of Adagrasib Drug-Drug Interactions

Adagrasib is a potent, highly selective, orally available, small molecule, covalent inhibitor of G12C mutated KRAS. As both a substrate and strong inhibitor of cytochrome P450 (CYP) 3A4, adagrasib inhibits its own CYP3A4-mediated metabolism following multiple dosing, resulting in time-dependent drug-drug interaction (DDI) liabilities. A physiologically-based pharmacokinetic (PBPK) model was developed and verified using a combination of physicochemical, in vitro and clinical pharmacokinetic (PK) data from healthy volunteers and cancer patients. The PBPK model well-described the single and multiple-dose adagrasib PK data as well as DDI data with itraconazole, rifampin, midazolam, warfarin, dextromethorphan, and digoxin, with model predictions within 1.5-fold of the observed clinical data. The PBPK model was used to predict untested scenarios including the clinical victim and perpetrator DDI liabilities at the approved dosing regimen of 600 mg twice daily (b.i.d.) in cancer patients. Strong, moderate, and weak inhibitors of CYP3A4 are predicted to have a negligible effect on the steady-state exposure of adagrasib 600 mg b.i.d. resulting from the significant inactivation of CYP3A4 by adagrasib. Additionally, strong and moderate inducers of CYP3A4 are predicted to decrease adagrasib exposure by 68% and 22%, respectively. As a perpetrator, adagrasib 600 mg b.i.d. is predicted to be a strong inhibitor of CYP3A4, a moderate inhibitor of CYP2C9 and CYP2D6, and an inhibitor of P-glycoprotein (P-gp). These results successfully supported regulatory interactions with the United States Food and Drug Administration regarding dosing recommendations for when adagrasib is used concomitantly with other medications, supporting a range of label claims in lieu of clinical trials.

Advancing understanding of human variability through toxicokinetic modeling, in vitro-in vivo extrapolation, and new approach methodologies

The merging of physiology and toxicokinetics, or pharmacokinetics, with computational modeling to characterize dosimetry has led to major advances for both the chemical and pharmaceutical research arenas. Driven by the mutual need to estimate internal exposures where in vivo data generation was simply not possible, the application of toxicokinetic modeling has grown exponentially in the past 30 years. In toxicology the need has been the derivation of quantitative estimates of toxicokinetic and toxicodynamic variability to evaluate the suitability of the tenfold uncertainty factor employed in risk assessment decision-making. Consideration of a host of physiologic, ontogenetic, genetic, and exposure factors are all required for comprehensive characterization. Fortunately, the underlying framework of physiologically based toxicokinetic models can accommodate these inputs, in addition to being amenable to capturing time-varying dynamics. Meanwhile, international interest in advancing new approach methodologies has fueled the generation of in vitro toxicity and toxicokinetic data that can be applied in in vitro-in vivo extrapolation approaches to provide human-specific risk-based information for historically data-poor chemicals. This review will provide a brief introduction to the structure and evolution of toxicokinetic and physiologically based toxicokinetic models as they advanced to incorporate variability and a wide range of complex exposure scenarios. This will be followed by a state of the science update describing current and emerging experimental and modeling strategies for population and life-stage variability, including the increasing application of in vitro-in vivo extrapolation with physiologically based toxicokinetic models in pharmaceutical and chemical safety research. The review will conclude with case study examples demonstrating novel applications of physiologically based toxicokinetic modeling and an update on its applications for regulatory decision-making. Physiologically based toxicokinetic modeling provides a sound framework for variability evaluation in chemical risk assessment.

Whole Body Physiologically Based Pharmacokinetic Model to Explain A Patient With Drug-Drug Interaction Between Voriconazole and Flucloxacillin

BACKGROUND AND OBJECTIVES

Voriconazole administered concomitantly with flucloxacillin may result in subtherapeutic plasma concentrations as shown in a patient with Staphylococcus aureus sepsis and a probable pulmonary aspergillosis. After switching our patient to posaconazole, therapeutic concentrations were reached. The aim of this study was to first test our hypothesis that flucloxacillin competes with voriconazole not posaconazole for binding to albumin ex vivo, leading to lower total concentrations in plasma.

METHODS

A physiologically based pharmacokinetic (PBPK) model was then applied to predict the mechanism of action of the drug-drug interaction (DDI). The model included non-linear hepatic metabolism and the effect of a severe infectious disease on cytochrome P450 (CYP) enzymes activity.

RESULTS

The unbound voriconazole concentration remained unchanged in plasma after adding flucloxacillin, thereby rejecting our hypothesis of albumin-binding site competition. The PBPK model was able to adequately predict the plasma concentration of both voriconazole and posaconazole over time in healthy volunteers. Upregulation of CYP3A4, CYP2C9, and CYP2C19 through the pregnane X receptor (PXR) gene by flucloxacillin resulted in decreased voriconazole plasma concentrations, reflecting the DDI observations in our patient. Posaconazole metabolism was not affected, or was only limitedly affected, by the changes through the PXR gene, which agrees with the observed plasma concentrations within the target range in our patient.

CONCLUSIONS

Ex vivo experiments reported that the unbound voriconazole plasma concentration remained unchanged after adding flucloxacillin. The PBPK model describes the potential mechanism driving the drug-drug and drug-disease interaction of voriconazole and flucloxacillin, highlighting the large substantial influence of flucloxacillin on the PXR gene and the influence of infection on voriconazole plasma concentrations, and suggests a more limited effect on other triazoles.

A physiologically based pharmacokinetic model for the transdermal uptake of semivolatile organic compounds from the atmosphere and through clothing

This study focuses on the semivolatile organic compound (SVOC) absorption through clothing and the skin. SVOCs are ubiquitous in daily life, in products like personal care items, plastics, and building materials. Understanding their permeation through the skin barrier is crucial for evaluating potential health risks of complete exposure. A PBPK model was developed to comprehend the dynamic interplay between SVOCs and human skin and to estimate tissue distribution throughout the body. The framework incorporated parameters such as skin permeability, physicochemical properties of the chemicals, and the impact of protective clothing and adsorbents. This model predicted the rate and extent of SVOC absorption under diverse scenarios. The PBPK predictions matched the experimental amount of mono-ethyl phthalate (MEP), a phthalate metabolite, when urine samples were collected for bare-skinned and clothed participants. Urine concentrations of MEP during a 6-hr exposure and for the next 48 hr show that clean clothing effectively decreased dermal uptake and the buildup of chemicals in the body. Additional removal of MEP was achieved through adsorption on activated carbon fabric. An increase in the maximum monolayer adsorption capacity or the Langmuir equilibrium constant further reduced the amount of MEP in the urine.

Development and validation of PBPK models for genistein and daidzein for use in a next-generation risk assessment

Introduction

All cosmetic ingredients must be evaluated for their safety to consumers. In the absence of in vivo data, systemic concentrations of ingredients can be predicted using Physiologically based Pharmacokinetic (PBPK) models. However, more examples are needed to demonstrate how they can be validated and applied in Next-Generation Risk Assessments (NGRA) of cosmetic ingredients. We used a bottom-up approach to develop human PBPK models for genistein and daidzein for a read-across NGRA, whereby genistein was the source chemical for the target chemical, daidzein.

Methods

An oral rat PBPK model for genistein was built using PK-Sim® and in vitro ADME input data. This formed the basis of the daidzein oral rat PBPK model, for which chemical-specific input parameters were used. Rat PBPK models were then converted to human models using human-specific physiological parameters and human in vitro ADME data. In vitro skin metabolism and penetration data were used to build the dermal module to represent the major route of exposure to cosmetics.

Results

The initial oral rat model for genistein was qualified since it predicted values within 2-fold of measured in vivo PK values. This was used to predict plasma concentrations from the in vivo NOAEL for genistein to set test concentrations in bioassays. Intrinsic hepatic clearance and unbound fractions in plasma were identified as sensitive parameters impacting the predicted Cmax values. Sensitivity and uncertainty analyses indicated the developed PBPK models had a moderate level of confidence. An important aspect of the development of the dermal module was the implementation of first-pass metabolism, which was extensive for both chemicals. The final human PBPK model for daidzein was used to convert the in vitro PoD of 33 nM (from an estrogen receptor transactivation assay) to an external dose of 0.2% in a body lotion formulation.

Conclusion

PBPK models for genistein and daidzein were developed as a central component of an NGRA read-across case study. This will help to gain regulatory confidence in the use of PBPK models, especially for cosmetic ingredients.

Modeling on in vivo disposition and cellular transportation of RNA lipid nanoparticles via quantum mechanics/physiologically-based pharmacokinetic approaches

The lipid nanoparticle (LNP) has been so far proven as a strongly effective delivery system for mRNA and siRNA. However, the mechanisms of LNP's distribution, metabolism, and elimination are complicated, while the transportation and pharmacokinetics (PK) of LNP are just sparsely investigated and simply described. This study aimed to build a model for the transportation of RNA-LNP in Hela cells, rats, mice, and humans by physiologically based pharmacokinetic (PBPK) and quantum mechanics (QM) models with integrated multi-source data. LNPs with different ionizable lipids, particle sizes, and doses were modeled and compared by recognizing their critical parameters dominating PK. Some interesting results were found by the models. For example, the metabolism of ionizable lipids was first limited by the LNP disassembly rate instead of the hydrolyzation of ionizable lipids; the ability of RNA release from endosomes for three ionizable lipids was quantitively derived and can predict the probability of RNA release. Moreover, the biodegradability of three ionizable lipids was estimated by the QM method and the is generally consistent with the result of PBPK result. In summary, the transportation model of RNA LNP among various species for the first time was successfully constructed. Various in vitro and in vivo pieces of evidence were integrated through QM/PBPK multi-level modeling. The resulting new understandings are related to biodegradability, safety, and RNA release ability which are highly concerned issues of the formulation. This would benefit the design and research of RNA-LNP in the future.

Prospective approaches to gene therapy computational modeling - spotlight on viral gene therapy

Clinical studies have found there still exists a lack of gene therapy dose-toxicity and dose-efficacy data that causes gene therapy dose selection to remain elusive. Model informed drug development (MIDD) has become a standard tool implemented throughout the discovery, development, and approval of pharmaceutical therapies, and has the potential to inform dose-toxicity and dose-efficacy relationships to support gene therapy dose selection. Despite this potential, MIDD approaches for gene therapy remain immature and require standardization to be useful for gene therapy clinical programs. With the goal to advance MIDD approaches for gene therapy, in this review we first provide an overview of gene therapy types and how they differ from a bioanalytical, formulation, route of administration, and regulatory standpoint. With this biological and regulatory background, we propose how MIDD can be advanced for AAV-based gene therapies by utilizing physiological based pharmacokinetic modeling and quantitative systems pharmacology to holistically inform AAV and target protein dynamics following dosing. We discuss how this proposed model, allowing for in-depth exploration of AAV pharmacology, could be the key the field needs to treat these unmet disease populations.

A Bayesian pharmacokinetics integrated phase I-II design to optimize dose-schedule regimes

The schedule of administering a drug has profound impact on the toxicity and efficacy profiles of the drug through changing its pharmacokinetics (PK). PK is an innate and indispensable component of the dose-schedule optimization. Motivated by this, we propose a Bayesian PK integrated dose-schedule finding (PKIDS) design to identify the optimal dose-schedule regime by integrating PK, toxicity, and efficacy data. Based on the causal pathway that dose and schedule affect PK, which in turn affects efficacy and toxicity, we jointly model the three endpoints by first specifying a Bayesian hierarchical model for the marginal distribution of the longitudinal dose-concentration process. Conditional on the drug concentration in plasma, we jointly model toxicity and efficacy as a function of the concentration. We quantify the risk-benefit of regimes using utility-continuously updating the estimates of PK, toxicity, and efficacy based on interim data-and make adaptive decisions to assign new patients to appropriate dose-schedule regimes via adaptive randomization. The simulation study shows that the PKIDS design has desirable operating characteristics.

A drug-drug interaction study and physiologically based pharmacokinetic modelling to assess the effect of an oral 5-lipoxygenase activating protein inhibitor on the pharmacokinetics of oral midazolam

AIMS

Early clinical studies have indicated that the pharmacokinetics of Atuliflapon (AZD5718) are time and dose dependent. The reason(s) for these findings is(are) not fully understood, but pre-clinical profiling suggests that time-dependent CYP3A4 inhibition cannot be excluded. In clinical practice, Atuliflapon will be co-administered with CYP3A4 substrates; thus, it is important to determine the impact of Atuliflapon on the pharmacokinetics (PK) of CYP3A4 substrates. The aim of this study was to evaluate the effect of Atuliflapon on the pharmacokinetics of a sensitive CYP3A4 substrate, midazolam, and to explore if the time-/dose-dependent effect seen after repeated dosing could be an effect of change in CYP3A4 activity.

METHODS

Open-label, fixed-sequence study in healthy volunteers to assess the PK of midazolam alone and in combination with Atuliflapon. Fourteen healthy male subjects received single oral dose of midazolam 2 mg on days 1 and 7 and single oral doses of Atuliflapon (125 mg) from days 2 to 7. A physiologically based pharmacokinetic (PBPK) model was developed to assess this drug-drug interaction.

RESULTS

Mean midazolam values of maximum plasma concentration (Cmax) and area under the curve (AUC) to infinity were increased by 39% and 56%, respectively, when co-administered with Atuliflapon vs. midazolam alone. The PBPK model predicted a 27% and 44% increase in AUC and a 23% and 35% increase in Cmax of midazolam following its co-administrations with two predicted therapeutically relevant doses of Atuliflapon.

CONCLUSIONS

Atuliflapon is a weak inhibitor of CYP3A4; this was confirmed by the validated PBPK model. This weak inhibition is predicted to have a minor PK effect on CYP3A4 metabolized drugs.

Infliximab in Inflammatory Bowel Disease: Leveraging Physiologically Based Pharmacokinetic Modeling in the Clinical Context

In this study, a physiologically based pharmacokinetic (PBPK) modeling framework was employed to explore infliximab exposure following intravenous (5 mg/kg) and subcutaneous administration (encompassing the approved 120 mg flat-fixed dose as a switching option) in virtual adult and pediatric patients with inflammatory bowel disease (IBD). The PBPK model and corresponding simulations were conducted using the PK-Sim® software platform. The PBPK simulation indicated that a 120 mg subcutaneous flat-fixed dose might not be optimal for heavier adults with IBD, suggesting the need for infliximab dose escalation. For an older virtual pediatric patient (14 years old), subcutaneous administration of a 120 mg flat-fixed dose appears to be a feasible IBD treatment option. In the final exploration scenario, the model was extended to predict hypothetical subcutaneous infliximab doses in a virtual pediatric population (6-18 years old), stratified into three weight bands (20-30 kg, 30-45 kg, and 45-70 kg), that would yield post-switch trough concentrations of infliximab comparable to those seen in adults with the 120 mg flat-fixed subcutaneous dose. The PBPK-model-informed dose suggestions were 40 mg for the 20-30 kg band, 80 mg for the 30-45 kg band, and 120 mg for the 45-70 kg band. As demonstrated in this paper, the PBPK modeling framework can serve as a versatile tool in clinical pharmacology to investigate various clinical scenarios, such as exploring alternative dosing regimens and routes of administration, ultimately advancing IBD treatment across diverse (sub)populations of clinical interest.

Dose Optimization Informed by PBPK Modeling: State-of-the Art and Future

Model-informed drug development (MIDD) is a powerful quantitative approach that plays an integral role in drug development and regulatory review. While applied throughout the life cycle of the development of new drugs, a key application of MIDD is to inform clinical trial design including dose selection and optimization. To date, physiologically-based pharmacokinetic (PBPK) modeling, an established component of the MIDD toolkit, has mainly been used for assessment of drug-drug interactions (DDIs) and consequential dose adjustments in regulatory submissions. As a result of recent scientific advances and growing confidence in the utility of the approach, PBPK models are being increasingly applied to provide dose recommendations for subjects with differing ages, genetics, and disease states. In this review, we present our perspective on the current landscape of regulatory acceptance of PBPK applications supported by relevant case studies. We also discuss the recent progress and future challenges associated with expanding the utility of PBPK models into emerging areas for regulatory decision making, especially dose optimization in highly vulnerable and understudied populations and facilitating diversity in clinical trials.

Modeling Metformin and Dapagliflozin Pharmacokinetics in Chronic Kidney Disease

Chronic kidney disease (CKD) is a complication of diabetes that affects circulating drug concentrations and elimination of drugs from the body. Multiple drugs may be prescribed for treatment of diabetes and co-morbidities, and CKD complicates the pharmacotherapy selection and dosing regimen. Characterizing variations in renal drug clearance using models requires large clinical datasets that are costly and time-consuming to collect. We propose a flexible approach to incorporate impaired renal clearance in pharmacokinetic (PK) models using descriptive statistics and secondary data with mechanistic models and PK first principles. Probability density functions were generated for various drug clearance mechanisms based on the degree of renal impairment and used to estimate the total clearance starting from glomerular filtration for metformin (MET) and dapagliflozin (DAPA). These estimates were integrated with PK models of MET and DAPA for simulations. MET renal clearance decreased proportionally with a reduction in estimated glomerular filtration rate (eGFR) and estimated net tubular transport rates. DAPA total clearance varied little with renal impairment and decreased proportionally to reported non-renal clearance rates. Net tubular transport rates were negative to partially account for low renal clearance compared with eGFR. The estimated clearance values and trends were consistent with MET and DAPA PK characteristics in the literature. Dose adjustment based on reduced clearance levels estimated correspondingly lower doses for MET and DAPA while maintaining desired dose exposure. Estimation of drug clearance rates using descriptive statistics and secondary data with mechanistic models and PK first principles improves modeling of CKD in diabetes and can guide treatment selection.

Drug-Drug Interactions between COVID-19 and Tuberculosis Medications: A Comprehensive Review of CYP450 and Transporter-Mediated Effects

Most medications undergo metabolism and elimination via CYP450 enzymes, while uptake and efflux transporters play vital roles in drug elimination from various organs. Interactions often occur when multiple drugs share CYP450-transporter-mediated metabolic pathways, necessitating a unique clinical care strategy to address the diverse types of CYP450 and transporter-mediated drug-drug interactions (DDI). The primary focus of this review is to record relevant mechanisms regarding DDI between COVID-19 and tuberculosis (TB) treatments, specifically through the influence of CYP450 enzymes and transporters on drug absorption, distribution, metabolism, elimination, and pharmacokinetics. This understanding empowers clinicians to prevent subtherapeutic and supratherapeutic drug levels of COVID medications when co-administered with TB drugs, thereby mitigating potential challenges and ensuring optimal treatment outcomes. A comprehensive analysis is presented, encompassing various illustrative instances of TB drugs that may impact COVID-19 clinical behavior, and vice versa. This review aims to provide valuable insights to healthcare providers, facilitating informed decision-making and enhancing patient safety while managing co-infections. Ultimately, this study contributes to the body of knowledge necessary to optimize therapeutic approaches and improve patient outcomes in the face of the growing challenges posed by infectious diseases.

Physiologically-based pharmacokinetic models versus allometric scaling for prediction of tyrosine-kinase inhibitor exposure from adults to children

PURPOSE

Model-based methods can predict pediatric exposure and support initial dose selection. The aim of this study was to evaluate the performance of allometric scaling of population pharmacokinetic (popPK) versus physiologically based pharmacokinetic (PBPK) models in predicting the exposure of tyrosine kinase inhibitors (TKIs) for pediatric patients (≥ 2 years), based on adult data. The drugs imatinib, sunitinib and pazopanib were selected as case studies due to their complex PK profiles including high inter-patient variability, active metabolites, time-varying clearances and non-linear absorption.

METHODS

Pediatric concentration measurements and adult popPK models were derived from the literature. Adult PBPK models were generated in PK-Sim® using available physicochemical properties, calibrated to adult data when needed. PBPK and popPK models for the pediatric populations were translated from the models for adults and were used to simulate concentration-time profiles that were compared to the observed values.

RESULTS

Ten pediatric datasets were collected from the literature. While both types of models captured the concentration-time profiles of imatinib, its active metabolite, sunitinib and pazopanib, the PBPK models underestimated sunitinib metabolite concentrations. In contrast, allometrically scaled popPK simulations accurately predicted all concentration-time profiles. Trough concentration (Ctrough) predictions from the popPK model fell within a 2-fold range for all compounds, while 3 out of 5 PBPK predictions exceeded this range for the imatinib and sunitinib metabolite concentrations.

CONCLUSION

Based on the identified case studies it appears that allometric scaling of popPK models is better suited to predict exposure of TKIs in pediatric patients ≥ 2 years. This advantage may be attributed to the stable enzyme expression patterns from 2 years old onwards, which can be easily related to adult levels through allometric scaling. In some instances, both methods performed comparably. Understanding where discrepancies between the model methods arise, can further inform model development and ultimately support pediatric dose selection.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

The use of quantitative clinical pharmacology approaches to support moxidectin dosing recommendations in lactation

Moxidectin is approved by the US Food and Drug Administration (US FDA) for the treatment of onchocerciasis (river-blindness) due to Onchocerca volvulus in patients aged 12 years and older. In onchocerciasis-endemic areas, mass drug administration (MDA) programs with ivermectin, with or without vector control, aim to control the disease, reduce morbidity, interrupt transmission, and more recently, achieve elimination. Moxidectin has the potential to be used in MDA programs. In countries where onchocerciasis is endemic, infants are often breastfed up to the age of 2 years, suggesting that some women are likely to be lactating during such periodic MDA programs. Quantitative analyses of non-clinical and clinical data using non-compartmental analysis and population based pharmacokinetic (popPK) modeling as well as physiologically based pharmacokinetic modeling (PBPK) were performed to determine the amount of moxidectin excreted in breast milk and subsequent exposures in the infant. The results of the analyses were similar. Concentrations of moxidectin in breast milk followed a similar pattern to those in plasma, with maximum concentrations occurring approximately 4 hours after dosing followed by a rapid decline in both breast milk and plasma. As early as two days after dosing, concentrations of moxidectin in breast milk were below the threshold for acceptable daily intake levels established by the European Medicines Agency (EMA) and FDA for secondary exposures from veterinary use, and below the WHO recommended relative infant dose (RID) safety threshold. The analyses were conducted to support prescribers and policy makers on dosing recommendations for moxidectin in lactation.

A whole-body mechanistic physiologically-based pharmacokinetic modeling of intravenous iron

Iron is essential for every cell of the mammalian organism. Iron deficiency is a major public health issue worldwide. Intravenous (IV) iron therapy has been used to treat anemia. However, IV iron therapy is known far away from ideal because the quantitative relationship between the pharmacokinetics and biodistribution of IV iron under different iron statuses remains unclear. Patients are known to suffer adverse effects from excessive iron accumulation. Our objective was to develop a physiologically based pharmacokinetic (PBPK) model of iron in mice and validate its application for predicting iron disposition in rats and humans. Previously published data on iron were collected for constructing the PBPK model of iron in mice, and then extrapolated to rats and humans based on physiologically and chemically specific parameters relevant to each species. The PBPK model characterized the distribution of iron in mice successfully. The model based on extrapolation to rats accurately simulated the ferric carboxymaltose (FCM) PK profiles in rat tissues. Similarly, the observed and simulated serum PK of FCM in humans were in reasonable agreement. This mechanistic whole-body PBPK model is useful for understanding and predicting iron effects on different species. It also establishes a foundation for future research that incorporates iron kinetics and biodistribution, along with related clinical experiments. This approach could lead to the development of effective and personalized iron deficiency anemia treatments.

Human Pharmacokinetic and CYP3A Drug-Drug Interaction Prediction of GDC-2394 Using Physiologically Based Pharmacokinetic Modeling and Biomarker Assessment

Physiologically based pharmacokinetic (PBPK) modeling was used to predict the human pharmacokinetics and drug-drug interaction (DDI) of GDC-2394. PBPK models were developed using in vitro and in vivo data to reflect the oral and intravenous PK profiles of mouse, rat, dog, and monkey. The learnings from preclinical PBPK models were applied to a human PBPK model for prospective human PK predictions. The prospective human PK predictions were within 3-fold of the clinical data from the first-in-human study, which was used to optimize and validate the PBPK model and subsequently used for DDI prediction. Based on the majority of PBPK modeling scenarios using the in vitro CYP3A induction data (mRNA and activity), GDC-2394 was predicted to have no-to-weak induction potential at 900 mg twice daily (BID). Calibration of the induction mRNA and activity data allowed for the convergence of DDI predictions to a narrower range. The plasma concentrations of the 4β-hydroxycholesterol (4β-HC) were measured in the multiple ascending dose study to assess the hepatic CYP3A induction risk. There was no change in plasma 4β-HC concentrations after 7 days of GDC-2394 at 900 mg BID. A dedicated DDI study found that GDC-2394 has no induction effect on midazolam in humans, which was reflected by the totality of predicted DDI scenarios. This work demonstrates the prospective utilization of PBPK for human PK and DDI prediction in early drug development of GDC-2394. PBPK modeling accompanied with CYP3A biomarkers can serve as a strategy to support clinical pharmacology development plans. SIGNIFICANCE STATEMENT: This work presents the application of physiologically based pharmacokinetic modeling for prospective human pharmacokinetic (PK) and drug-drug interaction (DDI) prediction in early drug development. The strategy taken in this report represents a framework to incorporate various approaches including calibration of in vitro induction data and consideration of CYP3A biomarkers to inform on the overall CYP3A-related DDI risk of GDC-2394.

Mathematical Modeling of Vedolizumab Treatment's Effect on Microbiota and Intestinal Permeability in Inflammatory Bowel Disease Patients

Growing evidence suggests that impaired gut permeability and gut microbiota alterations are involved in the pathogenesis of Inflammatory Bowel Diseases (IBDs), which include Ulcerative Colitis (UC) and Crohn's Disease (CD). Vedolizumab is an anti-α4β7 antibody approved for IBD treatment, used as the first treatment or second-line therapy when the first line results in inadequate effectiveness. The aim of this study is to develop a mathematical model capable of describing the pathophysiological mechanisms of Vedolizumab treatment in IBD patients. In particular, the relationship between drug concentration in the blood, colonic mucosal permeability and fecal microbiota composition was investigated and modeled to detect and predict trends in order to support and tailor Vedolizumab therapies. To pursue this aim, clinical data from a pilot study on a cluster of 11 IBD patients were analyzed. Enrolled patients underwent colonoscopy in three phases (before (t0), after 24 weeks of (t1) and after 52 weeks of (t2 ) Vedolizumab treatment) to collect mucosal biopsies for transepithelial electrical resistance (TEER) evaluation (permeability to ions), intestinal permeability measurement and histological analysis. Moreover, fecal samples were collected for the intestinal microbiota analysis at the three time points. The collected data were compared to those of 11 healthy subjects at t0, who underwent colonoscopy for screening surveillance, and used to implement a three-compartmental mathematical model (comprising central blood, peripheral blood and the intestine). The latter extends previous evidence from the literature, based on the regression of experimental data, to link drug concentration in the peripheral blood compartment with Roseburia abundance and intestinal permeability. The clinical data showed that Vedolizumab treatment leads to an increase in TEER and a reduction in intestinal permeability to a paracellular probe, improving tissue inflammation status. Microbiota analysis showed increasing values of Roseburia, albeit not statistically significant. This trend was adequately reproduced by the mathematical model, which offers a useful tool to describe the pathophysiological effects of Vedolizumab therapy on colonic mucosal permeability and fecal microbiota composition. The model's satisfactory predictive capabilities and simplicity shed light on the relationship between the drug, the microbiota and permeability and allow for its straightforward extension to diverse therapeutic conditions.

Determining tissue distribution of the oral antileishmanial agent miltefosine: a physiologically-based pharmacokinetic modeling approach

Miltefosine (MTS) is the only approved oral drug for treating leishmaniasis caused by intracellular Leishmania parasites that localize in macrophages of the liver, spleen, skin, bone marrow, and lymph nodes. MTS is extensively distributed in tissues and has prolonged elimination half-lives due to its high plasma protein binding, slow metabolic clearance, and minimal urinary excretion. Thus, understanding and predicting the tissue distribution of MTS help assess therapeutic and toxicologic outcomes of MTS, especially in special populations, e.g., pediatrics. In this study, a whole-body physiologically-based pharmacokinetic (PBPK) model of MTS was built on mice and extrapolated to rats and humans. MTS plasma and tissue concentration data obtained by intravenous and oral administration to mice were fitted simultaneously to estimate model parameters. The resulting high tissue-to-plasma partition coefficient values corroborate extensive distribution in all major organs except the bone marrow. Sensitivity analysis suggests that plasma exposure is most susceptible to changes in fraction unbound in plasma. The murine oral-PBPK model was further validated by assessing overlay of simulations with plasma and tissue profiles obtained from an independent study. Subsequently, the murine PBPK model was extrapolated to rats and humans based on species-specific physiological and drug-related parameters, as well as allometrically scaled parameters. Fold errors for pharmacokinetic parameters were within acceptable range in both extrapolated models, except for a slight underprediction in the human plasma exposure. These animal and human PBPK models are expected to provide reliable estimates of MTS tissue distribution and assist dose regimen optimization in special populations.

Physiologically based pharmacokinetic modelling of cefoperazone in paediatrics

AIMS

Cefoperazone is commonly used off-label in the treatment of bacterial meningitis and sepsis in children, and the pharmacokinetic (PK) data are limited in this vulnerable population. The goal of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict pediatric cefoperazone exposure for rational dosing recommendations.

METHODS

A cefoperazone PBPK model for adults was first constructed using Simcyp V22 simulator. Subsequently, the model was extended to children based on the built in age-dependent physiological parameters, while the drug characteristics remained unchanged. The verified pediatric PBPK model was then utilized to assess the rationality of the common dosing regimens for children at different age groups.

RESULTS

Cefoperazone PBPK model included elimination via biliary excretion, glomerular filtration, and organic anion transporter 3 (OAT3)-mediated tubular secretion. 95.2% of the observed mean concentrations and 100% of the area under the plasma drug concentration-time curve (AUC) and peak concentration (Cmax) in adults were within a twofold range of model mean predictions. Good predictive accuracy was also observed in children, including neonates. 50 mg/kg q12h cefoperazone demonstrated effective target attainment in virtual term neonates (<1 month) when the MIC was ≤1 mg/L, adhering to the stringent PK/PD target of 75% fT > MIC. 37.5 mg/kg q12h cefoperazone achieved the common 50% fT > MIC target for an MIC ≤ 0. 25 mg/L in virtual pediatric patients ranging from 1 month to 18 years of age.

CONCLUSIONS

A pediatric PBPK model was developed for cefoperazone, and it could serve as the basis for deriving rational dosing regimens in children.

Personalized Dosing of Medicines for Children: A Primer on Pediatric Pharmacometrics for Clinicians

The widespread use of drugs for unapproved purposes remains common in children, primarily attributable to practical, ethical, and financial constraints associated with pediatric drug research. Pharmacometrics, the scientific discipline that involves the application of mathematical models to understand and quantify drug effects, holds promise in advancing pediatric pharmacotherapy by expediting drug development, extending applications, and personalizing dosing. In this review, we delineate the principles of pharmacometrics, and explore its clinical applications and prospects. The fundamental aspect of any pharmacometric analysis lies in the selection of appropriate methods for quantifying pharmacokinetics and pharmacodynamics. Population pharmacokinetic modeling is a data-driven method ('top-down' approach) to approximate population-level pharmacokinetic parameters, while identifying factors contributing to inter-individual variability. Model-informed precision dosing is increasingly used to leverage population pharmacokinetic models and patient data, to formulate individualized dosing recommendations. Physiologically based pharmacokinetic models integrate physicochemical drug properties with biological parameters ('bottom-up approach'), and is particularly valuable in situations with limited clinical data, such as early drug development, assessing drug-drug interactions, or adapting dosing for patients with specific comorbidities. The effective implementation of these complex models hinges on strong collaboration between clinicians and pharmacometricians, given the pivotal role of data availability. Promising advancements aimed at improving data availability encompass innovative techniques such as opportunistic sampling, minimally invasive sampling approaches, microdialysis, and in vitro investigations. Additionally, ongoing research efforts to enhance measurement instruments for evaluating pharmacodynamics responses, including biomarkers and clinical scoring systems, are expected to significantly bolster our capacity to understand drug effects in children.

Penetration of Antibiotics into Subcutaneous and Intramuscular Interstitial Fluid: A Meta-Analysis of Microdialysis Studies in Adults

BACKGROUND AND OBJECTIVE

The interstitial fluid of tissues is the effect site for antibiotics targeting extracellular pathogens. Microdialysis studies investigating these concentrations in muscle and subcutaneous tissue have reported notable variability in tissue penetration. This study aimed to comprehensively summarise the existing data on interstitial fluid penetration in these tissues and to identify potential factors influencing antibiotic distribution.

METHODS

A literature review was conducted, focusing on subcutaneous and intramuscular microdialysis studies of antibiotics in both adult healthy volunteers and patients. Random-effect meta-analyses were used to aggregate effect size estimates of tissue penetration. The primary parameter of interest was the unbound penetration ratio, which represents the ratio of the area under the concentration-time curve in interstitial fluid relative to the area under the concentration-time curve in plasma, using unbound concentrations.

RESULTS

In total, 52 reports were incorporated into this analysis. The unbound antibiotic exposure in the interstitial fluid of healthy volunteers was, on average, 22% lower than in plasma. The unbound penetration ratio values were higher after multiple dosing but did not significantly differ between muscle and subcutaneous tissue. Unbound penetration ratio values were lower for acids and bases compared with neutral antibiotics. Neither the molecular weight nor the logP of the antibiotics accounted for the variations in the unbound penetration ratio. Obesity was associated with lower interstitial fluid penetration. Conditions such as sepsis, tissue inflammation and tissue ischaemia were not significantly associated with altered interstitial fluid penetration.

CONCLUSIONS

This study highlights the variability and generally lower exposure of unbound antibiotics in the subcutaneous and intramuscular interstitial fluid compared with exposure in plasma. Future research should focus on understanding the therapeutic relevance of these differences and identify key covariates that may influence them.

Physiologically based modeling of LNP-mediated delivery of mRNA in the vascular system

RNA therapeutics are an emerging, powerful class of drugs with potential applications in a wide range of disorders. A central challenge in their development is the lack of clear pharmacokinetic (PK)-pharmacodynamic relationship, in part due to the significant delay between the kinetics of RNA delivery and the onset of pharmacologic response. To bridge this gap, we have developed a physiologically based PK/pharmacodynamic model for systemically administered mRNA-containing lipid nanoparticles (LNPs) in mice. This model accounts for the physiologic determinants of mRNA delivery, active targeting in the vasculature, and differential transgene expression based on nanoparticle coating. The model was able to well-characterize the blood and tissue PKs of LNPs, as well as the kinetics of tissue luciferase expression measured by ex vivo activity in organ homogenates and bioluminescence imaging in intact organs. The predictive capabilities of the model were validated using a formulation targeted to intercellular adhesion molecule-1 and the model predicted nanoparticle delivery and luciferase expression within a 2-fold error for all organs. This modeling platform represents an initial strategy that can be expanded upon and utilized to predict the in vivo behavior of RNA-containing LNPs developed for an array of conditions and across species.

A Physiologically Based Pharmacokinetic (PBPK) Study to Assess the Adjuvanticity of Three Peptides in an Oral Vaccine

Following up on the first PBPK model for an oral vaccine built for alpha-tocopherol, three peptides are explored in this article to verify if they could support an oral vaccine formulation as adjuvants using the same PBPK modeling approach. A literature review was conducted to verify what peptides have been used as adjuvants in the last decades, and it was noticed that MDP derivatives have been used, with one of them even being commercially approved and used as an adjuvant when administered intravenously in oncology. The aim of this study was to build optimized models for three MDP peptides (MDP itself, MTP-PE, and murabutide) and to verify if they could act as adjuvants for an oral vaccine. Challenges faced by peptides in an oral delivery system are taken into consideration, and improvements to the formulations to achieve better results are described in a step-wise approach to reach the most-optimized model. Once simulations are performed, results are compared to determine what would be the best peptide to support as an oral adjuvant. According to our results, MTP-PE, the currently approved and commercialized peptide, could have potential to be incorporated into an oral formulation. It would be interesting to proceed with further in vivo experiments to determine the behavior of this peptide when administered orally with a proper formulation to overcome the challenges of oral delivery systems.

Development of Novel Methods for QSAR Modeling by Machine Learning Repeatedly: A Case Study on Drug Distribution to Each Tissue

Artificial intelligence is expected to help identify excellent candidates in drug discovery. However, we face a lack of data, as it is time-consuming and expensive to acquire raw data perfectly for many compounds. Hence, we tried to develop a novel quantitative structure-activity relationship (QSAR) method to predict a parameter more precisely from an incomplete data set via optimizing data handling by making use of predicted explanatory variables. As a case study we focused on the tissue-to-plasma partition coefficient (Kp), which is an important parameter for understanding drug distribution in tissues and building the physiologically based pharmacokinetic model and is a representative of small and sparse data sets. In this study, we predicted the Kp values of 119 compounds in nine tissues (adipose, brain, gut, heart, kidney, liver, lung, muscle, and skin), although some of these were not available. To fill the missing values in Kp for each tissue, first we predicted those Kp values by the nonmissing data set using a random forest (RF) model with in vitro parameters (log P, fu, Drug Class, and fi) like a classical prediction by a QSAR model. Next, to predict the tissue-specific Kp values in a test data set, we constructed a second RF model with not only in vitro parameters but also the Kp values of other tissues (i.e., other than target tissues) predicted by the first RF model as explanatory variables. Furthermore, we tested all possible combinations of explanatory variables and selected the model with the highest predictability from the test data set as the final model. The evaluation of Kp prediction accuracy based on the root-mean-square error and R2 value revealed that the proposed models outperformed other machine learning methods such as the conventional RF and message-passing neural networks. Significant improvements were observed in the Kp values of adipose tissue, brain, kidney, liver, and skin. These improvements indicated that the Kp information on other tissues can be used to predict the same for a specific tissue. Additionally, we found a novel relationship between each tissue by evaluating all combinations of explanatory variables. In conclusion, we developed a novel RF model to predict Kp values. We hope that this method will be applied to various problems in the field of experimental biology which often contains missing values in the near future.

Realizing the promise of Project Optimus: Challenges and emerging opportunities for dose optimization in oncology drug development

Project Optimus is a US Food and Drug Administration Oncology Center of Excellence initiative aimed at reforming the dose selection and optimization paradigm in oncology drug development. This project seeks to bring together pharmaceutical companies, international regulatory agencies, academic institutions, patient advocates, and other stakeholders. Although there is much promise in this initiative, there are several challenges that need to be addressed, including multidimensionality of the dose optimization problem in oncology, the heterogeneity of cancer and patients, importance of evaluating long-term tolerability beyond dose-limiting toxicities, and the lack of reliable biomarkers for long-term efficacy. Through the lens of Totality of Evidence and with the mindset of model-informed drug development, we offer insights into dose optimization by building a quantitative knowledge base integrating diverse sources of data and leveraging quantitative modeling tools to build evidence for drug dosage considering exposure, disease biology, efficacy, toxicity, and patient factors. We believe that rational dose optimization can be achieved in oncology drug development, improving patient outcomes by maximizing therapeutic benefit while minimizing toxicity.

The Use of Global Sensitivity Analysis to Assess the Oral Absorption of Weakly Basic Compounds: A Case Example of Dipyridamole

OBJECTIVE

To utilize the global system analysis (GSA) in oral absorption modeling to gain a deeper understanding of system behavior, improve model accuracy, and make informed decisions during drug development.

METHODS

GSA was utilized to give insight into which drug substance (DS), drug product (DP), and/or physiological parameter would have an impact on peak plasma concentration (Cmax) and area under the curve (AUC) of dipyridamole as a model weakly basic compound. GSA guided the design of in vitro experiments and oral absorption risk assessment using FormulatedProducts v2202.1.0. The solubility and precipitation profiles of dipyridamole in different bile salt concentrations were measured. The results were then used to build a mechanistic oral absorption model.

RESULTS

GSA warranted further investigation into the precipitation kinetics and its link to the levels of bile salt concentrations. Mechanistic modeling studies demonstrated that a precipitation-integrated modeling approach appropriately predicted the mean plasma profiles, Cmax, and AUC from the clinical studies.

CONCLUSIONS

This work shows the value of GSA utilization in early development to guide in vitro experimentation and build more confidence in identifying the critical parameters for the mathematical models.

Physiologically based pharmacokinetic modeling to predict the clinical effect of azole antifungal agents as CYP3A inhibitors on azelnidipine pharmacokinetics

In this study, a physiologically based pharmacokinetic (PBPK) model of the cytochrome P450 3A (CYP3A) substrate azelnidipine was developed using in vitro and clinical data to predict the effects of azole antifungals on azelnidipine pharmacokinetics. Modeling and simulations were conducted using the Simcyp™ PBPK simulator. The azelnidipine model consisted of a full PBPK model and a first-order absorption model. CYP3A was assumed as the only azelnidipine elimination route, and CYP3A clearance was optimized using the pharmacokinetic profile of single-dose 5-mg azelnidipine in healthy participants. The model reproduced the results of a clinical drug-drug interaction study and met validation criteria. PBPK model simulations using azole antifungals (itraconazole, voriconazole, posaconazole, fluconazole, fosfluconazole) and azelnidipine or midazolam (CYP3A index substrate) were performed. Increases in the simulated area under the plasma concentration-time curve from time zero extrapolated to infinity with inhibitors were comparable between azelnidipine (range, 2.11-6.47) and midazolam (range, 2.26-9.22), demonstrating that azelnidipine is a sensitive CYP3A substrate. Increased azelnidipine plasma concentrations are expected when co-administered with azole antifungals, potentially affecting azelnidipine safety. These findings support the avoidance of azole antifungals in patients taking azelnidipine and demonstrate the utility of PBPK modeling to inform appropriate drug use.

Predictive Modelling in pharmacokinetics: from in-silico simulations to personalized medicine

INTRODUCTION

Pharmacokinetic parameters assessment is a critical aspect of drug discovery and development, yet challenges persist due to limited training data. Despite advancements in machine learning and in-silico predictions, scarcity of data hampers accurate prediction of drug candidates' pharmacokinetic properties.

AREAS COVERED

The study highlights current developments in human pharmacokinetic prediction, talks about attempts to apply synthetic approaches for molecular design, and searches several databases, including Scopus, PubMed, Web of Science, and Google Scholar. The article stresses importance of rigorous analysis of machine learning model performance in assessing progress and explores molecular modeling (MM) techniques, descriptors, and mathematical approaches. Transitioning to clinical drug development, article highlights AI (Artificial Intelligence) based computer models optimizing trial design, patient selection, dosing strategies, and biomarker identification. In-silico models, including molecular interactomes and virtual patients, predict drug performance across diverse profiles, underlining the need to align model results with clinical studies for reliability. Specialized training for human specialists in navigating predictive models is deemed critical. Pharmacogenomics, integral to personalized medicine, utilizes predictive modeling to anticipate patient responses, contributing to more efficient healthcare system. Challenges in realizing potential of predictive modeling, including ethical considerations and data privacy concerns, are acknowledged.

EXPERT OPINION

AI models are crucial in drug development, optimizing trials, patient selection, dosing, and biomarker identification and hold promise for streamlining clinical investigations.

Designing inhaled small molecule drugs for severe respiratory diseases: an overview of the challenges and opportunities

INTRODUCTION

Inhaled drugs offer advantages for the treatment of respiratory diseases over oral drugs by delivering the drug directly to the lung, thus improving the therapeutic index. There is an unmet medical need for novel therapies for lung diseases, exacerbated by a multitude of challenges for the design of inhaled small molecule drugs.

AREAS COVERED

The authors review the challenges and opportunities for the design of inhaled drugs for respiratory diseases with a focus on new target discovery, medicinal chemistry, and pharmacokinetic, pharmacodynamic, and toxicological evaluation of drug candidates.

EXPERT OPINION

Inhaled drug discovery is facing multiple unique challenges. Novel biological targets are scarce, as is the guidance for medicinal chemistry teams to design compounds with inhalation-compatible features. It is exceedingly difficult to establish a PK/PD relationship given the complexity of pulmonary PK and the impact of physical properties of the drug substance on PK. PK, PD and toxicology studies are technically challenging and require large amounts of drug substance. Despite the current challenges, the authors foresee that the design of inhaled drugs will be facilitated in the future by our increasing understanding of pathobiology, emerging medicinal chemistry guidelines, advances in drug formulation, PBPK models, and in vitro toxicology assays.

Research Landscape of Physiologically Based Pharmacokinetic Model Utilization in Different Fields: A Bibliometric Analysis (1999-2023)

PURPOSE

The physiologically based pharmacokinetic (PBPK) modeling has received increasing attention owing to its excellent predictive abilities. However, there has been no bibliometric analysis about PBPK modeling. This research aimed to summarize the research development and hot points in PBPK model utilization overall through bibliometric analysis.

METHODS

We searched for publications related to the PBPK modeling from 1999 to 2023 in the Web of Science Core Collection (WoSCC) database. The Microsoft Office Excel, CiteSpace and VOSviewers were used to perform the analyses.

RESULTS

A total of 4,649 records from 1999 to 2023 were identified, and the largest number of publications focused in the period 2018-2023. The United States was the leading country, and the Environmental Protection Agency (EPA) was the leading institution. The journal Drug Metabolism and Disposition published and co-cited the most articles. Drug-drug interactions, special populations, and new drug development are the main topics in this research field.

CONCLUSION

We first visualize the research landscape and hotspots of the PBPK modeling through bibliometric methods. Our study provides a better understanding for researchers, especially beginners about the dynamization of PBPK modeling and presents the relevant trend in the future.

Predicting drug-drug interactions in breast cancer patients treated with CDK4/6 inhibitors and forward planning

INTRODUCTION

Cyclin-dependent kinase (CDK) 4/6 inhibitors are cornerstones in the treatment of Hormone Receptor (HR) positive and Human Epidermal Growth factor (HER2) negative metastatic breast cancer. Given their widespread use in the metastatic setting and emerging use in the adjuvant setting, studying drug-drug interactions (DDI) of these medications is of utmost importance.

AREAS COVERED

This review provides key background information on the CDK4/6 inhibitors, palbociclib, ribociclib, and abemaciclib. We discuss drug-drug interactions including those with proton pump inhibitors as well as CYP3A substrates, inhibitors, and inducers. We describe the effect of these drugs on membrane transporters and their substrates as well as those drugs that increase risk of CDK4/6 toxicities. Finally, we explore future directions for strategies to minimize drug-drug interactions.

EXPERT OPINION

It is crucial to be mindful of medications that may interfere with drug absorption, such as proton pump inhibitors, as well as those that interfere with drug metabolism, such as CYP3A4 inhibitors and inducers. Additionally, special consideration should be given to populations at higher risk for polypharmacy, such as older patients with greater comorbidities. These interactions and patient characteristics should be considered when developing individual treatment plans with CDK4/6 inhibitors.

Maximum likelihood estimation of renal transporter ontogeny profiles for pediatric PBPK modeling

Optimal treatment of infants with many renally cleared drugs must account for maturational differences in renal transporter (RT) activity. Pediatric physiologically-based pharmacokinetic (PBPK) models may incorporate RT activity, but this requires ontogeny profiles for RT activity in children, especially neonates, to predict drug disposition. Therefore, RT expression measurements from human kidney postmortem cortical tissue samples were normalized to represent a fraction of mature RT activity. Using these data, maximum likelihood estimated the distributions of RT activity across the pediatric age spectrum, including preterm and term neonates. PBPK models of four RT substrates (acyclovir, ciprofloxacin, furosemide, and meropenem) were evaluated with and without ontogeny profiles using average fold error (AFE), absolute average fold error (AAFE), and proportion of observations within the 5-95% prediction interval. Novel maximum likelihood profiles estimated ontogeny distributions for the following RT: OAT1, OAT3, OCT2, P-gp, URAT1, BCRP, MATE1, MRP2, MRP4, and MATE-2 K. Profiles for OAT3, P-gp, and MATE1 improved infant furosemide and neonate meropenem PBPK model AFE from 0.08 to 0.70 and 0.53 to 1.34 and model AAFE from 12.08 to 1.44 and 2.09 to 1.36, respectively, and improved the percent of data within the 5-95% prediction interval from 48% to 98% for neonatal ciprofloxacin simulations, respectively. Even after accounting for other critical population-specific maturational differences, novel RT ontogeny profiles substantially improved neonatal PBPK model performance, providing validated estimates of maturational differences in RT activity for optimal dosing in children.

Characterization and Applications of Permeabilized Hepatocytes in Drug Discovery

Hepatocytes are one of the most physiologically relevant in vitro liver systems for human translation of clearance and drug-drug interactions (DDI). However, the cell membranes of hepatocytes can limit the entry of certain compounds into the cells for metabolism and DDI. Passive permeability through hepatocytes can be different in vitro and in vivo, which complicates the human translation. Permeabilized hepatocytes offer a useful tool to probe mechanistic understanding of permeability-limited metabolism and DDI. Incubation with saponin of 0.01% at 0.5 million cells/mL and 0.05% at 5 million cells/mL for 5 min at 37°C completely permeabilized the plasma membrane of hepatocytes, while leaving the membranes of subcellular organelles intact. Permeabilized hepatocytes maintained similar enzymatic activity as intact unpermeabilized hepatocytes and can be stored at -80°C for at least 7 months. This approach reduces costs by preserving leftover hepatocytes. The relatively low levels of saponin in permeabilized hepatocytes had no significant impact on the enzymatic activity. As the cytosolic contents leak out from permeabilized hepatocytes, cofactors need to be added to enable metabolic reactions. Cytosolic enzymes will no longer be present if the media are removed after cells are permeabilized. Hence permeabilized hepatocytes with and without media removal may potentially enable reaction phenotyping of cytosolic enzymes. Although permeabilized hepatocytes work similarly as human liver microsomes and S9 fractions experimentally requiring addition of cofactors, they behave more like hepatocytes maintaining enzymatic activities for over 4 h. Permeabilized hepatocytes are a great addition to the drug metabolism toolbox to provide mechanistic insights.

Maternal Ezetimibe Concentrations Measured in Breast Milk and Its Use in Breastfeeding Infant Exposure Predictions

BACKGROUND

Lactating mothers taking ezetimibe, an antihyperlipidemic agent, may be hesitant to breastfeed despite the known benefit of breastfeeding to both mother and infant. Currently, no data exist on the presence or concentration of ezetimibe and its main active metabolite, ezetimibe-glucuronide (EZE-glucuronide), in human breast milk.

METHODS

Voluntary breast milk samples containing ezetimibe and EZE-glucuronide were attained from lactating mothers taking ezetimibe as part of their treatment. An assay was developed and validated to measure ezetimibe and EZE-glucuronide concentrations in breast milk. A workflow that utilized a developed and evaluated pediatric physiologically based pharmacokinetic (PBPK) model, the measured concentrations in milk, and weight-normalized breast milk intake volumes was applied to predict infant exposures and determine the upper area under the curve ratio (UAR).

RESULTS

Fifteen breast milk samples from two maternal-infant pairs were collected. The developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay showed an analytical range of 0.039-5.0 ng/mL and 0.39-50.0 ng/mL for ezetimibe and EZE-glucuronide, respectively. The measured concentrations in the breast milk samples were 0.17-1.02 ng/mL and 0.42-2.65 ng/mL of ezetimibe and EZE-glucuronide, respectively. The evaluated pediatric PBPK model demonstrated minimal exposure overlap in adult therapeutic dose and breastfed infant simulated area under the concentration-time curve from time zero to 24 h (AUC24). Calculated UAR across infant age groups ranged from 0.0015 to 0.0026.

CONCLUSIONS

PBPK model-predicted ezetimibe and EZE-glucuronide exposures and UAR suggest that breastfeeding infants would receive non-therapeutic exposures. Future work should involve a 'mother-infant pair study' to ascertain breastfed infant plasma ezetimibe and EZE-glucuronide concentrations to confirm the findings of this work.

Physiologically Based Pharmacokinetic (PBPK) Model Predictions of Disease Mediated Changes in Drug Disposition in Patients with Nonalcoholic Fatty Liver Disease (NAFLD)

PURPOSE

This study was designed to verify a virtual population representing patients with nonalcoholic fatty liver disease (NAFLD) to support the implementation of a physiologically based pharmacokinetic (PBPK) modeling approach for prediction of disease-related changes in drug pharmacokinetics.

METHODS

A virtual NAFLD patient population was developed in GastroPlus (v.9.8.2) by accounting for pathophysiological changes associated with the disease and proteomics-informed alterations in the abundance of metabolizing enzymes and transporters pertinent to drug disposition. The NAFLD population model was verified using exemplar drugs where elimination is influenced predominantly by cytochrome P450 (CYP) enzymes (chlorzoxazone, caffeine, midazolam, pioglitazone) or by transporters (rosuvastatin, 11C-metformin, morphine and the glucuronide metabolite of morphine).

RESULTS

PBPK model predictions of plasma concentrations of all the selected drugs and hepatic radioactivity levels of 11C-metformin were consistent with the clinically-observed data. Importantly, the PBPK simulations using the virtual NAFLD population model provided reliable estimates of the extent of changes in key pharmacokinetic parameters for the exemplar drugs, with mean predicted ratios (NAFLD patients divided by healthy individuals) within 0.80- to 1.25-fold of the clinically-reported values, except for midazolam (prediction-fold difference of 0.72).

CONCLUSION

A virtual NAFLD population model within the PBPK framework was successfully developed with good predictive capability of estimating disease-related changes in drug pharmacokinetics. This supports the use of a PBPK modeling approach for prediction of the pharmacokinetics of new investigational or repurposed drugs in patients with NAFLD and may help inform dose adjustments for drugs commonly used to treat comorbidities in this patient population.

Advancing Precision Medicine: A Review of Innovative In Silico Approaches for Drug Development, Clinical Pharmacology and Personalized Healthcare

The landscape of medical treatments is undergoing a transformative shift. Precision medicine has ushered in a revolutionary era in healthcare by individualizing diagnostics and treatments according to each patient's uniquely evolving health status. This groundbreaking method of tailoring disease prevention and treatment considers individual variations in genes, environments, and lifestyles. The goal of precision medicine is to target the "five rights": the right patient, the right drug, the right time, the right dose, and the right route. In this pursuit, in silico techniques have emerged as an anchor, driving precision medicine forward and making this a realistic and promising avenue for personalized therapies. With the advancements in high-throughput DNA sequencing technologies, genomic data, including genetic variants and their interactions with each other and the environment, can be incorporated into clinical decision-making. Pharmacometrics, gathering pharmacokinetic (PK) and pharmacodynamic (PD) data, and mathematical models further contribute to drug optimization, drug behavior prediction, and drug-drug interaction identification. Digital health, wearables, and computational tools offer continuous monitoring and real-time data collection, enabling treatment adjustments. Furthermore, the incorporation of extensive datasets in computational tools, such as electronic health records (EHRs) and omics data, is also another pathway to acquire meaningful information in this field. Although they are fairly new, machine learning (ML) algorithms and artificial intelligence (AI) techniques are also resources researchers use to analyze big data and develop predictive models. This review explores the interplay of these multiple in silico approaches in advancing precision medicine and fostering individual healthcare. Despite intrinsic challenges, such as ethical considerations, data protection, and the need for more comprehensive research, this marks a new era of patient-centered healthcare. Innovative in silico techniques hold the potential to reshape the future of medicine for generations to come.

Integrating the gut microbiome and pharmacology

The gut microbiome harbors trillions of organisms that contribute to human health and disease. These bacteria can also affect the properties of medical drugs used to treat these diseases, and drugs, in turn, can reshape the microbiome. Research addressing interdependent microbiome-host-drug interactions thus has broad impact. In this Review, we discuss these interactions from the perspective of drug bioavailability, absorption, metabolism, excretion, toxicity, and drug-mediated microbiome modulation. We survey approaches that aim to uncover the mechanisms underlying these effects and opportunities to translate this knowledge into new strategies to improve the development, administration, and monitoring of medical drugs.

A minimal PBPK model to accelerate preclinical development of drugs against tuberculosis

Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework. Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b. Results: The model has been successfully tested and validated against 11 anti-TB compounds-rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices. Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Local Identifiability Analysis, Parameter Subset Selection and Verification for a Minimal Brain PBPK Model

Physiologically-based pharmacokinetic (PBPK) modeling is important for studying drug delivery in the central nervous system, including determining antibody exposure, predicting chemical concentrations at target locations, and ensuring accurate dosages. The complexity of PBPK models, involving many variables and parameters, requires a consideration of parameter identifiability; i.e., which parameters can be uniquely determined from data for a specified set of concentrations. We introduce the use of a local sensitivity-based parameter subset selection algorithm in the context of a minimal PBPK (mPBPK) model of the brain for antibody therapeutics. This algorithm is augmented by verification techniques, based on response distributions and energy statistics, to provide a systematic and robust technique to determine identifiable parameter subsets in a PBPK model across a specified time domain of interest. The accuracy of our approach is evaluated for three key concentrations in the mPBPK model for plasma, brain interstitial fluid and brain cerebrospinal fluid. The determination of accurate identifiable parameter subsets is important for model reduction and uncertainty quantification for PBPK models.

Recent Deep Learning Applications to Structure-Based Drug Design

Identification and optimization of small molecules that bind to and modulate protein function is a crucial step in the early stages of drug development. For decades, this process has benefitted greatly from the use of computational models that can provide insights into molecular binding affinity and optimization. Over the past several years, various types of deep learning models have shown great potential in improving and enhancing the performance of traditional computational methods. In this chapter, we provide an overview of recent deep learning-based developments with applications in drug discovery. We classify these methods into four subcategories dependent on the task each method is aiming to solve. For each subcategory, we provide the general framework of the approach and discuss individual methods.

A tutorial for model-based evaluation and translation of cardiovascular safety in preclinical trials

Assessment of drug-induced effects on the cardiovascular (CV) system remains a critical component of the drug discovery process enabling refinement of the therapeutic index. Predicting potential drug-related unintended CV effects in the preclinical stage is necessary for first-in-human dose selection and preclusion of adverse CV effects in the clinical stage. According to the current guidelines for small molecules, nonclinical CV safety assessment conducted via telemetry analyses should be included in the safety pharmacology core battery studies. However, the manual for quantitative evaluation of the CV safety signals in animals is available only for electrocardiogram parameters (i.e., QT interval assessment), not for hemodynamic parameters (i.e., heart rate, blood pressure, etc.). Various model-based approaches, including empirical pharmacokinetic-toxicodynamic analyses and systems pharmacology modeling could be used in the framework of telemetry data evaluation. In this tutorial, we provide a comprehensive workflow for the analysis of nonclinical CV safety on hemodynamic parameters with a sequential approach, highlight the challenges associated with the data, and propose respective solutions, complemented with a reproducible example. The work is aimed at helping researchers conduct model-based analyses of the CV safety in animals with subsequent translation of the effect to humans seamlessly and efficiently.

Estimation of volatile organic compound exposure concentrations and time to reach a specific dermal absorption using physiologically based pharmacokinetic modeling

A procedure was proposed to estimate dermal exposures based on a physiologically based pharmacokinetic (PBPK) model developed in rats. The study examined vapor concentrations ranging from 500 to 10,000 ppm for dibromomethane and 2,500 to 40,000 ppm for bromochloromethane. These concentrations were reconstructed based on chemical blood levels measured in 4 hr, with errors varying from 0.0% to 52.0%. The PBPK approach adequately predicted the blood concentrations and helped simulate contaminant transport through the stratum corneum and distribution in the body compartments. The proposed technique made it possible to estimate the skin absorption time (SAT) obtained from acute inhalation toxicity data. An inverse relationship exists between the SAT and exposure concentration. The method can be helpful in toxicology and risk assessment of hazardous volatile organic compounds.