Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective

Microfluidic blood-milk barrier and physiologically based pharmacokinetic model to predict lofexidine secretion into breast milk

INTRODUCTION

Lofexidine (LUCEMYRA®) is the only FDA-approved, non-opioid, non-addictive treatment for opioid withdrawal symptoms, crucial for postpartum and pregnant women affected by the opioid crisis. Despite its clinical importance, data on its secretion into breast milk is limited. This study aims to develop a novel, microfluidic-based blood-milk-barrier on a chip model, a static human mammary cell transwell model, and a physiologically based pharmacokinetic (PBPK) lactation model to estimate the breast milk secretion of lofexidine, thereby ensuring maternal and infant safety and improving withdrawal management.

METHODS

A novel microfluidic device was developed to build a mammary epithelium-on-a-chip model, and a transwell plate was used to develop a static mammary epithelium using a human noncarcinogenic mammary epithelial cell (MEC) population that can form an integrated barrier with tight junctions. Both models were used to evaluate the transfer of lofexidine through the in vitro mammary cell barrier. The fraction of unbound lofexidine in the breast milk was determined by a Rapid Equilibrium Dialysis (RED) assay. Eleven approaches, including a novel, previously published in vitro to in vivo extrapolation (IVIVE) approach and various other approaches, were used to estimate milk-to-plasma (M/P) ratios of lofexidine. A whole-body lactation PBPK model was built using Simcyp® simulator v22 and used to predict the concentration-time profiles of lofexidine in both human plasma and breast milk.

RESULTS

A subpopulation of human normal mammary epithelial MCF10A cells (named MCF10A-TJ) was identified to form an integrated barrier that reaches trans-epithelial electrical resistance (TEER) values of over 1000 Ω·cm2 by culturing with in-house designed maintenance and boosting medium. The microfluidic device-based mammary epithelium-on-a-chip model generated slightly higher lofexidine permeability values than the static transwell mammary epithelial cell model. The predicted milk-to-plasma (M/P) ratio of lofexidine ranged from 0.40 to 15.88. Four approaches estimated an M/P ratio below 1, while seven predicted values above 1, mostly between 1.35 and 5.48. The whole-body lactation PBPK model predicted the concentration-time profile of lofexidine in breast milk, with an estimated M/P ratio of approximately 2.0. This value falls within the mid-range of the predictions obtained from all eleven methods.

CONCLUSION

This study introduces comprehensive and novel approaches to predict lofexidine secretion into breast milk. Most predictions suggest higher lofexidine concentration in milk than in plasma, raising potential safety concerns for opioid withdrawal management. Further pharmacokinetic clinical lactation studies are needed to validate these predictions.

Physiologically Based Pharmacokinetic Modeling of Tofacitinib: Predicting Drug Exposure and Optimizing Dosage in Special Populations and Drug-Drug Interaction Scenarios

Background: Tofacitinib is mainly used in the adult population for immune-mediated inflammatory diseases. There is little information available on the pharmacokinetics of tofacitinib in pediatric patients, populations with hepatic impairment and renal impairment, and patients with drug-drug interactions (DDIs). This study aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the pharmacokinetics of tofacitinib in the populations mentioned above. Methods: We developed the PBPK models in PK-Sim® and evaluated the models with observed clinical PK data. The Monte Carlo algorithm was used for parameter identification. Results: The adult PBPK model accurately simulated the pharmacokinetic profiles of all administration scenarios. The geometric mean fold errors for the predicted/observed maximum concentration and area under the curve are 1.17 and 1.16, respectively. The extrapolated models accurately simulated the pharmacokinetic characteristics of tofacitinib. The pediatric patients aged 12-to-<18 years and 2-to-<6 years need to adjust the dose to 4 mg BID and 1.7 mg BID, respectively, to achieve comparable steady-state exposures to 5 mg BID in adults. The populations with moderate hepatic impairment and severe renal impairment need to reduce the dose to 50% and 75% of the original dose, respectively. Tofacitinib should be reduced to 50% and 65% of the original dose for concomitant use with fluconazole and ketoconazole, respectively, and increased to 150% of the original dose for concomitant use with rifampicin. Conclusions: We developed a tofacitinib PBPK model and extrapolated it to special populations and DDIs. The predictive results of the models can help the rational use of tofacitinib in these populations.

Industry Perspectives on Implementation of Model Master File (MMF) Framework for Generics and Innovator Drugs: Opportunities, Challenges and Future Outlook

Modeling and simulation (M&S) based approaches have proven significant utility in both new drug and generic product development. Considering the plethora of applications of such novel approaches, the concept of model master file (MMF) has been introduced recently to streamline the regulatory submission process as well as to facilitate the use of M&S approaches. The MMF has potential to reduce the applicant's efforts in preparing and submitting modeling-based applications and can result in reduced review timelines. Approved MMF's are considered as reusable, sharable, portable and generalizable and thus can be used by the same applicant in multiple submissions or by multiple applicants. To further increase the understanding of the MMF framework and to understand potential applications, and limitations, the USFDA and the Center for Research on Complex Generics (CRCG, https://www.complexgenerics.org ) co-hosted a hybrid public workshop titled "Considerations and Potential Regulatory Applications for a Model Master File". This article summarizes the industry perspectives of MMF implementation from both new drug and generic product development perspectives. With the help of diverse case studies, an effort was made in the manuscript to discuss potential challenges, opportunities and benefits. The objective of this article is to portray industry thinking on the MMF concept and the use and implementation of the concept during drug discovery and development. The views presented in this manuscript are of industry participants present at the workshop and not the industry at large.

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.

Opportunities and Challenges of Hepatic Impairment Physiologically Based Pharmacokinetic Modeling in Drug Development-An IQ Perspective

PBPK models are gaining special interest as a drug development tool to estimate the effect of intrinsic and extrinsic factors on drug pharmacokinetics. The IQ Consortium Clinical Pharmacology Organ Impairment Working Group conducted a survey across IQ Consortium member pharmaceutical companies to understand current practices on how PBPK is used in understanding the effect of hepatic impairment (HI) on drug disposition and its impact on clinical development. Responses from 21 participants indicated that most organizations (~86%) are already using PBPK models for HI assessment. The survey results indicate that PBPK models have been influential in optimizing the design of dedicated HI study with 57% of respondents using PBPK models to inform the design elements of dedicated HI studies, and the majority of these respondents using the PBPK model to support internal decision making regarding the HI study. Additionally, the PBPK model was used by 62% of the respondents to predict drug plasma protein binding. Despite common usage of the PBPK models by drug developers, 14.3% of the respondents discussed their PBPK modeling strategy with regulatory agencies with only two cases where the regulators accepted the PBPK model. In conclusion, although the use of PBPK models to support regulatory decisions regarding drug use in HI is currently limited, its future is promising, and the success of such models needs collaboration between regulators and drug developers to shrink the knowledge gap in the use of PBPK as an impactful tool for drug development in patients with HI.

Molecular Precision Medicine: Application of Physiologically Based Pharmacokinetic Modeling to Predict Drug-Drug Interactions Between Lidocaine and Rocuronium/Propofol/Paracetamol

The perioperative period, encompassing preoperative, intraoperative, and postoperative phases, is crucial for comprehensive patient care. During this time, the use of opioids and other drugs can lead to drug-drug interactions (DDIs), potentially resulting in adverse drug reactions (ADRs) that increase morbidity, mortality, and healthcare costs. This study investigates the drug-drug interactions (DDIs) between rocuronium, propofol, paracetamol, and lidocaine, focusing on the CYP-mediated metabolism of these drugs in the perioperative context, where these drugs are frequently co-administered. Using physiologically based pharmacokinetic (PBPK) modeling through the GastroPlus™ software and in vitro experiments with Hep G2 cells, we aimed to assess potential toxicities and pharmacokinetic interactions. Cellular viability assays revealed significant toxicity when lidocaine was combined with propofol and rocuronium, while paracetamol exhibited no considerable impact on viability. PBPK simulations confirmed moderate interactions with rocuronium and weak interactions with propofol but no relevant interactions with paracetamol. These findings emphasize the need for dose adjustments in perioperative settings to enhance patient safety, particularly with propofol and rocuronium, while supporting the co-administration of lidocaine and paracetamol. These findings show the importance of moving towards a personalized medicine model, adjusting the clinical use of lidocaine according to individual patient needs, thus promoting safer and more effective perioperative care and moving beyond the "one-size-fits-all" approach in anesthetic management.

Impact of Food Physical Properties on Oral Drug Absorption: A Comprehensive Review

Food-Drug Interaction (FDI) refers to the phenomenon where food affects the pharmacokinetic or pharmacodynamic characteristics of a drug, significantly altering the drug's absorption rate or absorption extent. These Interactions are considered as a primary determinant in influencing the bioavailability of orally administered drugs within the gastrointestinal tract. The impact of food on drug absorption is complex and multifaceted, potentially involving alterations in gastrointestinal physiology, increases in splanchnic blood flow rates, and shifts in the gut microbiota's composition. Up to now, extensive research has focused on the interactions between food composition (such as proteins, fats, and vitamins) and drug absorption. In contrast, the impact of food physical properties (such as viscosity, volume, and pH) has received less attention in drug development. This article reviewed the impact of food properties on oral drug absorption based on a comprehensive literature search, focusing on the influence of food volume and food viscosity. From the perspective of pharmacokinetics, we examined interaction trends between food properties and drugs across different classification based on the Biopharmaceutics Classification System (BCS). In addition, we introduced the practical application of physiologically based pharmacokinetic (PBPK) modeling in predicting oral drug absorption under the influence of food Properties.

Utility of physiologically based pharmacokinetic modeling in predicting and characterizing clinical drug interactions

Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic dynamic modeling approach that can be used to predict or retrospectively describe changes in drug exposure due to drug-drug interactions (DDIs). With advancements in commercially available PBPK software, PBPK DDI modeling has become a mainstream approach from early drug discovery through to late-stage drug development and is often used to support regulatory packages for new drug applications. This Minireview will briefly describe the approaches to predicting DDI using PBPK and static modeling approaches, the basic model structures and features inherent to PBPK DDI models, and key examples where PBPK DDI models have been used to describe complex DDI mechanisms. Future directions aimed at using PBPK models to characterize transporter-mediated DDI, predict DDI in special populations, and assess the DDI potential of protein therapeutics will be discussed. A summary of the 209 PBPK DDI examples published to date in 2023 will be provided. Overall, current data and trends suggest a continued role for PBPK models in the characterization and prediction of DDI for therapeutic molecules. SIGNIFICANCE STATEMENT: Physiologically based pharmacokinetic (PBPK) models have been a key tool in the characterization of various pharmacokinetic phenomena, including drug-drug interactions. This Minireview will highlight recent advancements and publications around physiologically based pharmacokinetic drug-drug interaction modeling, an important area of drug discovery and development research in light of the increasing prevalence of polypharmacology in clinical settings.

Static Versus Dynamic Model Predictions of Competitive Inhibitory Metabolic Drug-Drug Interactions via Cytochromes P450: One Step Forward and Two Steps Backwards

BACKGROUND

Predicting metabolic drug-drug interactions (DDIs) via cytochrome P450 enzymes (CYP) is essential in drug development, but controversy has reemerged recently about whether in vitro-in vivo extrapolation (IVIVE) using static models can replace dynamic models for some regulatory filings and label recommendations.

OBJECTIVE

The aim of this study was to determine if static and dynamic models are equivalent for the quantitative prediction of metabolic DDIs arising from competitive CYP inhibition.

METHODS

Drug parameter spaces were varied to simulate 30,000 DDIs between hypothetical substrates and inhibitors of CYP3A4. Predicted area under the plasma concentration-time profile ratios for substrates (AUCr = AUC(presence of precipitant)/AUC(absence of precipitant)) were compared between dynamic simulations (Simcyp® V21) and corresponding static calculations, giving an inter-model discrepancy ratio (IMDR = AUCrdynamic/AUCrstatic). Dynamic simulations were conducted using a 'population' representative and a 'vulnerable patient' representative with maximal concentration (Cmax) or average steady-state concentration (Cavg,ss) as the inhibitor driver concentrations. IMDRs outside the interval 0.8-1.25 were defined as discrepancy between models.

RESULTS

The highest rate of IMDR <0.8 and IMDR >1.25 discrepancies in the 'population' representative was 85.9% and 3.1%, respectively, when using Cavg,ss as the inhibitor driver concentration. Using the 'vulnerable patient' representative showed the highest rate of IMDR >1.25 discrepancies at 37.8%.

CONCLUSION

Static models are not equivalent to dynamic models for predicting metabolic DDIs via competitive CYP inhibition across diverse drug parameter spaces, particularly for vulnerable patients. Caution is warranted in drug development if static IVIVE approaches are used alone to evaluate metabolic DDI risks.

The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases

Autoimmune diseases (AIDs) are a group of disorders in which the immune system attacks the body's own tissues, leading to chronic inflammation and organ damage. These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. In recent years, pharmacometrics has emerged as a critical tool in drug discovery and development (DDD) and precision medicine. The aim of this review is to explore the diverse roles that pharmacometrics has played in addressing the challenges associated with DDD and personalized therapies in the treatment of AIDs. Methods: This review synthesizes research from the past two decades on pharmacometric methodologies, including Physiologically Based Pharmacokinetic (PBPK) modeling, Pharmacokinetic/Pharmacodynamic (PK/PD) modeling, disease progression (DisP) modeling, population modeling, model-based meta-analysis (MBMA), and Quantitative Systems Pharmacology (QSP). The incorporation of artificial intelligence (AI) and machine learning (ML) into pharmacometrics is also discussed. Results: Pharmacometrics has demonstrated significant potential in optimizing dosing regimens, improving drug safety, and predicting patient-specific responses in AIDs. PBPK and PK/PD models have been instrumental in personalizing treatments, while DisP and QSP models provide insights into disease evolution and pathophysiological mechanisms in AIDs. AI/ML implementation has further enhanced the precision of these models. Conclusions: Pharmacometrics plays a crucial role in bridging pre-clinical findings and clinical applications, driving more personalized and effective treatments for AIDs. Its integration into DDD and translational science, in combination with AI and ML algorithms, holds promise for advancing therapeutic strategies and improving autoimmune patients' outcomes.

Experiences and initiatives on pharmacokinetic modeling and simulation data analysis: Perspectives from the Brazilian Health Regulatory Agency (ANVISA)

The landscape of drug product development and regulatory sciences is evolving, driven by the increasing application of systems thinking and modeling and simulation (M&S) techniques, especially from a biopharmaceutics perspective. Patient-centric quality standards can be achieved within this context through the application of quality by design (QbD) principles and M&S, specifically by defining clinically relevant dissolution specifications (CRDS). To this end, it is essential to bridge in vitro results to drug product in vivo performance, emphasizing the need to explore the translational capacity of biopharmaceutics tools. Physiologically based M&S analyses offer a unique avenue for integrating the drug, drug product, and biological properties of a target organism to study their interactions on the pharmacokinetic response. Accordingly, Physiologically Based Biopharmaceutics Modeling (PBBM) has seen increasing use to support drug development and regulatory applications globally. In Brazil, a Model-Informed Drug Development (MIDD) policy and strategic project are not yet established, limiting applicability of M&S techniques. Drawing from the experience of the ANVISA-Academia PBBM Working Group (WG), this article assesses the opportunities and challenges for pharmacometrics (PMx) in Brazil and proposes strategies to advance the adoption of M&S analyses into regulatory decision-making.

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.

Physiologically-based pharmacokinetic model of in vitro porcine ear skin permeation for drug delivery research

In silico techniques, such as physiologically based pharmacokinetic modeling (PBKP), are recently gaining importance. Computational methods in drug discovery and development and the generic drugs industry enhance research effectiveness by saving time and money and avoiding ethical issues. One key advantage is the ability to conduct toxicology studies without risking harm to living beings. This study aimed to repurpose the multi-phase multi-layer mechanistic dermal absorption (MPML MechDermA) PBPK model for simulation permeation through porcine ear skin under in vitro conditions. The work was divided into four steps: (1) the development of a pig ear skin model based on a previously collected dataset; (2) testing the model's ability to discriminate permeation between pig ear, human abdomen, and human back skin; (3) development of a caffeine permeation model; and (4) testing the caffeine model's performance against in vitro generated data sourced from the scientific literature. Data from 31 manuscripts were used for the development of the pig skin model. Based on these data, values specific to pig skin were found for 22 parameters of the MPML MechDermA model. The model was able to discriminate permeation between pig and human skin. A caffeine model was developed and used to simulate seven experiments identified in the literature. The model's performance was assessed by comparing simulated to observed results. Based on a visual check, all simulations were considered acceptable, whereas three out of seven experiments met the twofold difference criterion. The variability of the experimental data was considered the biggest challenge for reliable model assessment.

Model-Based Dose Selection of a Sphingosine-1-Phosphate Modulator, Etrasimod, in Patients with Various Degrees of Hepatic Impairment

BACKGROUND/OBJECTIVES

Etrasimod is a newly FDA-approved Sphingosine-1-Phosphate modulator indicated for moderate and severe ulcerative colitis. It is extensively metabolized in the liver via the cytochrome P450 system and may accumulate markedly in patients with hepatic dysfunction, exposing them to toxicity. The aim of the current study is to utilize a physiologically-based pharmacokinetic modeling approach to evaluate the impact of hepatic impairment on the pharmacokinetic behavior of etrasimod and to appropriately select dosage regimens for patients with chronic liver disease; Methods: PK-Sim was used to develop the etrasimod PBPK model, which was verified using clinical data from healthy subjects and subsequently adapted to reflect the physiological changes associated with varying degrees of hepatic dysfunction; Results: Simulations indicated that hepatic clearance of etrasimod is clearly reduced in patients with Child-Pugh B and C liver impairment. Based on these findings, dosing adjustments were proposed to achieve therapeutic exposures equivalent to those in individuals with normal liver function. In the Child-Pugh B and C population groups, 75% and 62.5%, respectively, of the standard dose were enough to have comparable exposure to the healthy population. These adjusted dosages aim to mitigate the risk of drug toxicity while maintaining efficacy; Conclusions: The PBPK model provides a robust framework for individualizing drug therapy in patients with hepatic impairment, ensuring safer and more effective treatment outcomes. Further clinical studies are warranted to verify these dosing recommendations and to refine the model for broader clinical applications.

Recent advances in environmental toxicology: Exploring gene editing, organ-on-a-chip, chimeric animals, and in silico models

In our daily life, we are exposed to various environmental pollutants in multiple ways. At present, we mainly rely on animal models and two-dimensional cell culture models to evaluate the toxicity of environmental pollutants. Nevertheless, results in animal models do not always apply to humans because of differences between species, while two-dimensional cell culture models cannot replicate the in vivo microenvironments, making it difficult to predict the true toxic response of environmental pollutants in humans. The development of various high-end technologies in recent years has provided new opportunities for environmental toxicology research. The application of these high-end technologies in environmental toxicology can complement the limitations of traditional environmental toxicology screening and more accurately predict the toxicity of environmental pollutants. In this review, we first introduce the advantages and disadvantages of traditional environmental toxicology methods, then review the principles and development of four high-end technologies, such as gene editing, organ-on-a-chip, chimeric animals, and in silico models, summarize their application in toxicity testing, and finally emphasize their importance/potential in environmental toxicology.

Coproporphyrin-I as a Selective OATP1B Biomarker Can Be Used to Delineate the Mechanisms of Complex Drug-Drug Interactions: Cedirogant Case Study

Cedirogant is an inverse agonist of retinoic acid-related orphan receptor gamma thymus developed for the treatment of chronic plaque psoriasis. Cedirogant induces cytochrome P450 (CYP) 3A4 while inhibiting P-glycoprotein (P-gp), breast cancer resistance protein (BCRP), organic anion transporting polypeptide (OATP) 1B1, and OATP1B3 in vitro. Static drug-drug interactions (DDIs) predictions suggested possible clinical induction of CYP3A4, and inhibition of P-gp, BCRP, and OATP1B1, leading to challenges in interpreting DDI studies between cedirogant and substrates of CYP3A, P-gp, BCRP, and OATP1B1/3. Here the effects of cedirogant on the pharmacokinetics of two statin drugs were investigated in healthy participants. Coproporphyrin-I (CP-I), a selective endogenous OATP1B biomarker, was used to assess the impact of cedirogant on OATP1B. Cedirogant (375 mg once daily) increased rosuvastatin maximum plasma concentration (Cmax) and area under the plasma concentration curve (AUCtau) by 141% and 55%, respectively when co-administered, whereas atorvastatin Cmax increased by 40% with no effect on its AUCtau compared with administration of rosuvastatin/atorvastatin alone. Cedirogant did not increase CP-I exposures, indicating no clinical OATP1B inhibition. The increased rosuvastatin exposure and minimal change in atorvastatin exposure with co-administration of cedirogant is attributed to BCRP inhibition and interplay between P-gp/BCRP inhibition and CYP3A induction, respectively. Correlation analysis with data from two investigational drugs (glecaprevir and flubentylosin) demonstrated that OATP1B1 R-value of > 1.5 and [Cmax,u]/[OATP1B1 IC50] of > 0.1 are associated with > 1.25-fold increase in CP-I Cmax ratio. This demonstrates the utility of CP-I in disentangling mechanisms underlying a complex DDI involving multiple transporters and enzymes and proposes refined criteria for static OATP1B inhibition predictions.

Investigation of a fully mechanistic physiologically based pharmacokinetics model of absorption to support predictions of milk concentrations in breastfeeding women and the exposure of infants: A case study for albendazole

Due to limited non-clinical and clinical data, European guidance recommends to discontinue breastfeeding when taking albendazole. The aim of this study was to consider the use of PBPK modeling to support the expected exposure in breastfed infants. A fully mechanistic PBPK approach was used to provide quantitative predictions of albendazole and its main active metabolite, albendazole sulfoxide, concentrations in plasma and breast milk of lactating women. The model predicted the exposure in adults and the large food effect, however, it does not predict all the clinical data for the exposure in children. Milk/plasma ratio predictions were also largely over-predicted for this lipophilic compound, but not for the less lipophilic metabolite. Predictions using the observed ratio and a worse-case exposure based on Cmax predictions, suggest doses to children through milk will be low. However, more clinical data are required before full exposure predictions can be made to breastfed infants.

Modeling and Simulations in Latin-American Generic Markets: Perspectives from Chilean Local Industry, Regulatory Agency, and Academia

In the last 20 years, modeling and simulations (M&S) have gained increased attention in pharmaceutical sciences. International industry and world-reference agencies have used M&S to make cost-efficient decisions through the model-informed drug development (MIDD) framework. Modeling tools include biopredictive dissolution models, physiologically based pharmacokinetic models (PBPK), biopharmaceutic models (PBBM), and virtual bioequivalence, among many others. Regulatorily, health agencies are becoming more and more open to accept the use of M&S to support regulatory applications, including setting dissolution specifications, quality-by-design (QbD), postapproval changes (SUPAC), etc. Nonetheless, the potential of M&S has been only barely explored in Latin America (Latam) across different actors: industry, regulatory agencies, and even academia. In this manuscript, we discuss the challenges and opportunities for implementing M&S approaches in Latam. Perspectives of regional experts were shared in a workshop. Attendance (professionals from industry, regulator, academia, and clinicians) also shared their views via survey. The rational development of bioequivalent generics was considered the main opportunity for M&S in regional market, particularly the use of PBPK and PBBM. Nonetheless, a critical mass of modeling scientists is needed before Latin American industry and regulators can actually benefit from M&S. Collaborations (e.g., Academia-Industry and Academia-Regulatory) may be a path to develop applied research projects and train the future modelers. Reaching that critical mass, scientists from industry may apply modeling across generic drug development process and life cycle, while regulatory scientists may issue guidelines in local language to support regional industry. Only at that stage could the full potential of MIDD be reached in Latin American generic markets.

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.

Addressing health equity for breastfeeding women: primaquine for Plasmodium vivax radical cure

Plasmodium vivax malaria remains a global health challenge, with approximately 6.9 million estimated cases in 2022. The parasite has a dormant liver stage, the hypnozoite, which reactivates to cause repeated relapses over weeks, months, or years. These relapses erode patient health, contribute to the burden of malaria, and promote transmission. Radical cure to prevent relapses requires administration of an 8-aminoquinoline, either primaquine or tafenoquine. However, malaria treatment guidelines updated by the World Health Organization (WHO) in October 2023 restrict primaquine use for women breastfeeding children < 6 months of age, or women breastfeeding older children if their child is G6PD deficient or if the child's G6PD status is unknown. Primaquine restrictions assume that 8-aminoquinoline exposures in breast milk would be sufficient to cause haemolysis in the nursing infant should they be G6PD deficient. WHO recommendations for tafenoquine are awaited. Notably, the WHO recommends that infants are breastfed for the first 2 years of life, and exclusively until 6 months old. Repeated pregnancies, followed by extended breastfeeding leaves women in P. vivax endemic regions potentially vulnerable to relapses for many years. This puts women's health at risk, increases the malaria burden, and perpetuates transmission, hindering malaria control and elimination. The benefits of lifting restrictions on primaquine administration to breastfeeding women are significant, avoiding the adverse consequences of repeated episodes of acute malaria, such as severe anaemia. Recent data challenge the restriction of primaquine in breastfeeding women. Clinical pharmacokinetic data in breastfeeding infants ≥ 28 days old show that the exposure to primaquine is very low and less than 1% of the maternal exposure, indicating negligible risk to infants, irrespective of their G6PD status. Physiologically-based pharmacokinetic modelling complements the clinical data, predicting minimal primaquine exposure to infants and neonates via breast milk from early post-partum. This article summarizes the clinical and modelling evidence for a favourable benefit:risk evaluation of P. vivax radical cure with primaquine for breastfeeding women without the need for infant G6PD testing, supporting a change in policy. This adjustment to current treatment guidelines would support health equity in regard to effective interventions to protect women and their children, enhance malaria control strategies, and advance P. vivax elimination.

Model-Informed Radiopharmaceutical Therapy Optimization: A Study on the Impact of PBPK Model Parameters on Physical, Biological, and Statistical Measures in 177Lu-PSMA Therapy

Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time-activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes.

Aging-Related CYP3A Functional Changes in Chinese Older Patients: New Findings from Model-Based Assessment of Amlodipine

Aging-related alterations in hepatic enzyme activity, particularly of the CYP3A, significantly impact drug efficacy and safety in older adults, making it essential to understand how aging affects CYP function for optimal drug therapy. The exogenous probe substrate method, a minimally invasive approach to assess liver metabolic enzyme activity in vivo, is effective in studying these changes. Amlodipine being extensively metabolized (> 90%) in the liver, primarily via cytochrome P450 enzyme CYP3A was selected as a probe to investigate and quantify the factors affecting the aging-related changes of CYP3A in the Chinese older population. Amlodipine concentration data were collected from an ongoing noninterventional clinical study conducted at Peking University Third Hospital. A physiologically-based pharmacokinetic modeling approach, grounded in population pharmacokinetic (PPK) analysis, was employed to physiologically quantify the aging-related changes in CYP3A function. A total of 132 amlodipine concentrations from 69 patients were obtained from the clinical study. PPK analysis shows that frailty phenotype but not age is a significant influence and frail patients have 37% greater plasma amlodipine exposure than nonfrail patients. This difference in CYP3A function may be attributed to a 63.2% lower CYP3A relative abundance in the frail patients, compared with that in the nonfrail patients. In the context of dose selection for older adults, focusing on frailty rather than chronological age should be recognized as a more relevant approach, because frailty might more accurately reflect the individual's biological age. Our study suggested a need to shift the research focus from chronological age to biological age.

Physiologically Based Pharmacokinetic Modeling and Clinical Extrapolation for Topical Application of Pilocarpine on Eyelids: A Comprehensive Study

Exploiting a convenient and highly bioavailable ocular drug delivery approach is currently one of the hotspots in the pharmaceutical industry. Eyelid topical application is seen to be a valuable strategy in the treatment of chronic ocular diseases. To further elucidate the feasibility of eyelid topical administration as an alternative route for ocular drug delivery, pharmacokinetic and pharmacodynamic studies of pilocarpine were conducted in rabbits. Besides, a novel physiologically based pharmacokinetic (PBPK) model describing eyelid transdermal absorption and ocular disposition was developed in rabbits. The PBPK model of rabbits was extrapolated to human by integrating the drug-specific permeability parameters and human physiological parameters to predict ocular pharmacokinetic in human. After eyelid topical application of pilocarpine, the concentration of pilocarpine in iris peaked at 2 h with the value of 18,724 ng/g and the concentration in aqueous humor peaked at 1 h with the value of 1,363 ng/mL. Significant miotic effect were observed from 0.5 h to 4.5 h after eyelid topical application of pilocarpine in rabbits, while that were observed from 0.5 h to 3.5 h after eyedrop instillation. The proposed eyelid PBPK model was capable of reasonably predicting ocular exposure of pilocarpine after application on the eyelid skin and based on the PBPK model, the human ocular concentration was predicted to be 10-fold lower than that in rabbits. And it was suggested that drugs applied on the eyelid skin could transfer into the eyeball through corneal pathway and scleral pathway. This work could provide pharmacokinetic and pharmacodynamic data for the development of eyelid drug delivery, as well as the reference for clinical applications.

Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity

In recent years, virtual drug study, as an emerging research strategy, has become increasingly important in guiding and promoting new drug research and development. Researchers can integrate a variety of technical methods to improve the efficiency of all phases of new drug research and development, including the use of artificial intelligence, modeling and simulation for target identification, compound screening and pharmacokinetic characteristics evaluation, and the application of clinical trial simulation to carry out clinical research. This paper aims to elaborate on the application of virtual drug study in the key stages of new drug research and development and discuss the opportunities and challenges it faces in supporting new drug research and development.

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.

Docetaxel, cyclophosphamide, and epirubicin: application of PBPK modeling to gain new insights for drug-drug interactions

The new adjuvant chemotherapy of docetaxel, epirubicin, and cyclophosphamide has been recommended for treating breast cancer. It is necessary to investigate the potential drug-drug Interactions (DDIs) since they have a narrow therapeutic window in which slight differences in exposure might result in significant differences in treatment efficacy and tolerability. To guide clinical rational drug use, this study aimed to evaluate the DDI potentials of docetaxel, cyclophosphamide, and epirubicin in cancer patients using physiologically based pharmacokinetic (PBPK) models. The GastroPlus™ was used to develop the PBPK models, which were refined and validated with observed data. The established PBPK models accurately described the pharmacokinetics (PKs) of three drugs in cancer patients, and the predicted-to-observed ratios of all the PK parameters met the acceptance criterion. The PBPK model predicted no significant changes in plasma concentrations of these drugs during co-administration, which was consistent with the observed clinical phenomenon. Besides, the verified PBPK models were then used to predict the effect of other Cytochrome P450 3A4 (CYP3A4) inhibitors/inducers on these drug exposures. In the DDI simulation, strong CYP3A4 modulators changed the exposure of three drugs by 0.71-1.61 fold. Therefore, patients receiving these drugs in combination with strong CYP3A4 inhibitors should be monitored regularly to prevent adverse reactions. Furthermore, co-administration of docetaxel, cyclophosphamide, or epirubicin with strong CYP3A4 inducers should be avoided. In conclusion, the PBPK models can be used to further investigate the DDI potential of each drug and to develop dosage recommendations for concurrent usage by additional perpetrators or victims.

Overview of preclinical and clinical studies investigating pharmacokinetics and drug-drug interactions of padsevonil

BACKGROUND

Padsevonil is an antiseizure medication candidate intended to benefit patients with drug-resistant epilepsy. Our investigations aimed at characterizing pharmacokinetics and drug-drug interaction (DDI) profile of padsevonil.

RESEARCH DESIGN AND METHODS

An overview of preclinical and clinical pharmacology studies conducted during padsevonil development is provided.

RESULTS

In preclinical studies, cytochrome (CYP) 3A4 was identified as the main P450 isoform involved in padsevonil metabolism, with potential minor contribution from CYP2C19. Padsevonil was shown to be a time-dependent CYP2C19-inhibitor, weak CYP3A4-inducer, weak inhibitor of P-gp/OCT1/MATE2-K, and potent OCT2-inhibitor. Initial clinical pharmacology studies in healthy participants showed that padsevonil had (i) good absorption, (ii) clearance mediated mainly by metabolism, and (iii) time-dependent kinetics. A study in genotyped participants confirmed the role of CYP2C19 in clearance and time-dependent kinetics; the major contribution of CYP3A4 was confirmed in DDI studies with CYP3A4-inducers (carbamazepine, oxcarbazepine) and -inhibitor (erythromycin). Padsevonil did not affect pharmacokinetics of valproate/lamotrigine/levetiracetam/oxcarbazepine or oral contraceptives. In a cocktail clinical study, padsevonil showed moderate CYP2C19 inhibition (omeprazole) and weak CYP3A4 induction (oral midazolam). No specific effects on CYP1A2 (caffeine), CYP2C9 (S-warfarin), and CYP2D6 (dextromethorphan) were observed.

CONCLUSIONS

The studies presented helped in understanding padsevonil disposition and risks of DDIs, which would inform dosing and prescribing.

CLINICAL TRIAL REGISTRATION

https://www.clinicaltrials.gov identifiers are NCT04131517, NCT03480243, NCT03695094, NCT04075409.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations. Some considerations are put forth for the future improvements of MIDD including refining regulatory considerations, improving the integrity of physiological data, applying the emerging technologies, and exploring the application of MIDD in new therapies like gene therapies for special populations.

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.

Advances in Modeling Approaches for Oral Drug Delivery: Artificial Intelligence, Physiologically-Based Pharmacokinetics, and First-Principles Models

Oral drug absorption is the primary route for drug administration. However, this process hinges on multiple factors, including the drug's physicochemical properties, formulation characteristics, and gastrointestinal physiology. Given its intricacy and the exorbitant costs associated with experimentation, the trial-and-error method proves prohibitively expensive. Theoretical models have emerged as a cost-effective alternative by assimilating data from diverse experiments and theoretical considerations. These models fall into three categories: (i) data-driven models, encompassing classical pharmacokinetics, quantitative-structure models (QSAR), and machine/deep learning; (ii) mechanism-based models, which include quasi-equilibrium, steady-state, and physiologically-based pharmacokinetics models; and (iii) first principles models, including molecular dynamics and continuum models. This review provides an overview of recent modeling endeavors across these categories while evaluating their respective advantages and limitations. Additionally, a primer on partial differential equations and their numerical solutions is included in the appendix, recognizing their utility in modeling physiological systems despite their mathematical complexity limiting widespread application in this field.

Perceived barriers and facilitators for model-informed dosing in pregnancy: a qualitative study across healthcare practitioners and pregnant women

BACKGROUND

Most women use medication during pregnancy. Pregnancy-induced changes in physiology may require antenatal dose alterations. Yet, evidence-based doses in pregnancy are missing. Given historically limited data, pharmacokinetic models may inform pregnancy-adjusted doses. However, implementing model-informed doses in clinical practice requires support from relevant stakeholders.

PURPOSE

To explore the perceived barriers and facilitators for model-informed antenatal doses among healthcare practitioners (HCPs) and pregnant women.

METHODS

Online focus groups and interviews were held among healthcare practitioners (HCPs) and pregnant women from eight countries across Europe, Africa and Asia. Purposive sampling was used to identify pregnant women plus HCPs across various specialties prescribing or providing advice on medication to pregnant women. Perceived barriers and facilitators for implementing model-informed doses in pregnancy were identified and categorised using a hybrid thematic analysis.

RESULTS

Fifty HCPs and 11 pregnant women participated in 12 focus groups and 16 interviews between January 2022 and March 2023. HCPs worked in the Netherlands (n = 32), the UK (n = 7), South Africa (n = 5), Uganda (n = 4), Kenya, Cameroon, India and Vietnam (n = 1 each). All pregnant women resided in the Netherlands. Barriers and facilitators identified by HCPs spanned 14 categories across four domains whereas pregnant women described barriers and facilitators spanning nine categories within the same domains. Most participants found current antenatal dosing information inadequate and regarded model-informed doses in pregnancy as a valuable and for some, much-needed addition to antenatal care. Although willingness-to-follow model-informed antenatal doses was high across both groups, several barriers for implementation were identified. HCPs underlined the need for transparent model validation and endorsement of the methodology by recognised institutions. Foetal safety was deemed a critical knowledge gap by both groups. HCPs' information needs and preferred features for model-informed doses in pregnancy varied. Several pregnant women expressed a desire to access information and partake in decisions on antenatal dosing.

CONCLUSIONS

Given the perceived limitations of current pharmacotherapy for pregnant women and foetuses, model-informed dosing in pregnancy was seen as a promising means to enhance antenatal care by pregnant women and healthcare practitioners.

Physiologically Based Biopharmaceutics Modeling for Gefapixant IR Formulation Development and Defining the Bioequivalence Dissolution Safe Space

Gefapixant is a weakly basic drug which has been formulated as an immediate release tablet for oral administration. A physiologically based biopharmaceutics model (PBBM) was developed based on gefapixant physicochemical properties and clinical pharmacokinetics to aid formulation selection, bioequivalence safe space assessment and dissolution specification settings. In vitro dissolution profiles of different free base and citrate salt formulations were used as an input to the model. The model was validated against the results of independent studies, which included a bioequivalence and a relative bioavailability study, as well as a human ADME study, all meeting acceptance criteria of prediction errors ≤ 20% for both Cmax and AUC.  PBBM was also applied to evaluate gastric pH-mediated drug-drug-interaction potential with co-administration of a proton pump inhibitor (PPI), omeprazole. Model results showed good agreement with clinical data in which omeprazole lowered gefapixant exposure for the free base formulation but did not significantly alter gefapixant pharmacokinetics for the citrate based commercial drug product. An extended virtual dissolution bioequivalence safe space was established.  Gefapixant drug product batches are anticipated to be bioequivalent with the clinical reference batch when their dissolution is > 80% in 60 minutes. PBBM established a wide dissolution bioequivalence space as part of assuring product quality.

Defining the next generation of severe malaria treatment: a target product profile

BACKGROUND

Severe malaria is a life-threatening infection, particularly affecting children under the age of 5 years in Africa. Current treatment with parenteral artemisinin derivatives is highly efficacious. However, artemisinin partial resistance is widespread in Southeast Asia, resulting in delayed parasite clearance after therapy, and has emerged independently in South America, Oceania, and Africa. Hence, new treatments for severe malaria are needed, and it is prudent to define their characteristics now. This manuscript focuses on the target product profile (TPP) for new treatments for severe malaria. It also highlights preparedness when considering ways of protecting the utility of artemisinin-based therapies.

TARGET PRODUCT PROFILE

Severe malaria treatments must be highly potent, with rapid onset of antiparasitic activity to clear the infection as quickly as possible to prevent complications. They should also have a low potential for drug resistance selection, given the high parasite burden in patients with severe malaria. Combination therapies are needed to deter resistance selection and dissemination. Partner drugs which are approved for uncomplicated malaria treatment would provide the most rapid development pathway for combinations, though new candidate molecules should be considered. Artemisinin combination approaches to severe malaria would extend the lifespan of current therapy, but ideally, completely novel, non-artemisinin-based combination therapies for severe malaria should be developed. These should be advanced to at least phase 2 clinical trials, enabling rapid progression to patient use should current treatment fail clinically. New drug combinations for severe malaria should be available as injectable formulations for rapid and effective treatment, or as rectal formulations for pre-referral intervention in resource-limited settings.

CONCLUSION

Defining the TPP is a key step to align responses across the community to proactively address the potential for clinical failure of artesunate in severe malaria. In the shorter term, artemisinin-based combination therapies should be developed using approved or novel drugs. In the longer term, novel combination treatments should be pursued. Thus, this TPP aims to direct efforts to preserve the efficacy of existing treatments while improving care and outcomes for individuals affected by this life-threatening disease.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules

INTRODUCTION

Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary.

AREAS COVERED

This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review.

EXPERT OPINION

ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

Evaluating Drug Interactions between Ritonavir and Opioid Analgesics: Implications from Physiologically Based Pharmacokinetic Simulation

Several commonly used opioid analgesics, such as fentanyl, sufentanil, alfentanil, and hydrocodone, are by report primarily metabolized by the CYP3A4 enzyme. The concurrent use of ritonavir, a potent CYP3A4 inhibitor, can lead to significant drug interactions. Using physiologically based pharmacokinetic (PBPK) modeling and simulation, this study examines the effects of different dosing regimens of ritonavir on the pharmacokinetics of these opioids. The findings reveal that co-administration of ritonavir significantly increases the exposure of fentanyl analogs, with over a 10-fold increase in the exposure of alfentanil and sufentanil when given with ritonavir. Conversely, the effect of ritonavir on fentanyl exposure is modest, likely due to additional metabolism pathways. Additionally, the study demonstrates that the steady-state exposure of hydrocodone and its active metabolite hydromorphone can be increased by up to 87% and 95%, respectively, with concurrent use of ritonavir. The extended-release formulation of hydrocodone is particularly affected. These insights from PBPK modeling provide valuable guidance for optimizing opioid dosing and minimizing the risk of toxicity when used in combination with ritonavir-containing prescriptions.

Assessing and mitigating pH-mediated DDI risks in drug development - formulation approaches and clinical considerations

pH-mediated drug-drug interactions (DDI) is a prevalent DDI in drug development, especially for weak base compounds with highly pH-dependent solubility. FDA has released a guidance on the evaluation of pH-mediated DDI assessments using in vitro testing and clinical studies. Currently, there is no common practice of ways of testing across the academia and industry. The development of biopredictive method and physiologically-based biopharmaceutics modeling (PBBM) approaches to assess acid-reducing agent (ARA)-DDI have been proven with accurate prediction and could decrease drug development burden, inform clinical design and potentially waive clinical studies. Formulation strategies and careful clinical design could help mitigate the pH-mediated DDI to avoid more clinical studies and label restrictions, ultimately benefiting the patient. In this review paper, a detailed introduction on biorelevant dissolution testing, preclinical and clinical study requirement and PBPK modeling approaches to assess ARA-DDI are described. An improved decision tree for pH-mediated DDI is proposed. Potential mitigations including clinical or formulation strategies are discussed.

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 in Pregnancy, during Lactation and in Neonates: Achievements, Challenges and Future Directions

Obstetric subjects represent a special population in pharmacology [...].

Applications of Advanced Natural Language Processing for Clinical Pharmacology

Natural language processing (NLP) is a branch of artificial intelligence, which combines computational linguistics, machine learning, and deep learning models to process human language. Although there is a surge in NLP usage across various industries in recent years, NLP has not been widely evaluated and utilized to support drug development. To demonstrate how advanced NLP can expedite the extraction and analyses of information to help address clinical pharmacology questions, inform clinical trial designs, and support drug development, three use cases are described in this article: (1) dose optimization strategy in oncology, (2) common covariates on pharmacokinetic (PK) parameters in oncology, and (3) physiologically-based PK (PBPK) analyses for regulatory review and product label. The NLP workflow includes (1) preparation of source files, (2) NLP model building, and (3) automation of data extraction. The Clinical Pharmacology and Biopharmaceutics Summary Basis of Approval (SBA) documents, US package inserts (USPI), and approval letters from the US Food and Drug Administration (FDA) were used as our source data. As demonstrated in the three example use cases, advanced NLP can expedite the extraction and analyses of large amounts of information from regulatory review documents to help address important clinical pharmacology questions. Although this has not been adopted widely, integrating advanced NLP into the clinical pharmacology workflow can increase efficiency in extracting impactful information to advance drug development.

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.

Development of a physiologically based pharmacokinetic model for levetiracetam in patients with renal impairment to guide dose adjustment based on steady-state peak/trough concentrations

Levetiracetam may cause acute renal failure and myoclonic encephalopathy at high plasma levels, particularly in patients with renal impairment. The aim of this study was to develop a physiologically based pharmacokinetic (PBPK) model to predict levetiracetam pharmacokinetics in Chinese adults with epilepsy and renal impairment and define appropriate levetiracetam dosing regimen.PBPK models for healthy subjects and epilepsy patients with renal impairment were developed, validated, and adapted. Furthermore, we predicted the steady-state trough and peak concentrations of levetiracetam in patients with renal impairment using the final PBPK model, thereby recommending appropriate levetiracetam dosing regimens for different renal function stages. The predicted maximum plasma concentration (Cmax), time to maximum concentration (Tmax), area under the plasma concentration-time curve (AUC) were in agreement (0.8 ≤ fold error ≤ 1.2) with the observed, and the fold error of the trough concentrations in end-stage renal disease (ESRD) was 0.77 - 1.22. The prediction simulations indicated that the recommended doses of 1000, 750, 500, and 500 mg twice daily for epilepsy patients with mild, moderate, severe renal impairment, and ESRD, respectively, were sufficient to achieve the target plasma concentration of levetiracetam.

Physiologically based pharmacokinetic modeling of ritonavir-oxycodone drug interactions and its implication for dosing strategy

The concomitant administration of ritonavir and oxycodone may significantly increase the plasma concentrations of oxycodone. This study was aimed to simulate DDI between ritonavir and oxycodone, a widely used opioid, and to formulate dosing protocols for oxycodone by using physiologically based pharmacokinetic (PBPK) modeling. We developed a ritonavir PBPK model incorporating induction and competitive and time-dependent inhibition of CYP3A4 and competitive inhibition of CYP2D6. The ritonavir model was evaluated with observed clinical pharmacokinetic data and validated for its CYP3A4 inhibition potency. We then used the model to simulate drug interactions between oxycodone and ritonavir under various dosing scenarios. The developed model captured the pharmacokinetic characteristics of ritonavir from clinical studies. The model also accurately predicts exposure changes of midazolam, triazolam, and oxycodone in the presence of ritonavir. According to model simulations, the steady-state maximum, minimum and average concentrations of oxycodone increased by up to 166% after co-administration with ritonavir, and the total exposure increased by approximately 120%. To achieve similar steady-state concentrations, halving the dose with an unchanged dosing interval or doubling the dosing interval with an unaltered single dose should be practical for oxycodone, whether formulated in uncoated or controlled-release tablets during long-term co-medication with ritonavir. The results revealed exposure-related risks of oxycodone-ritonavir interactions that have not been studied clinically and emphasized PBPK as a workable method to direct judicious dosage.

A multiscale modeling study of nanoparticle-based targeting therapy against atherosclerosis

Although researches on nanoparticle-based (NP-based) drug delivery system for atherosclerosis treatment have grown rapidly in recent years, there are limited studies in quantifying the effects of targeting drugs on plaque components and microenvironment. The purpose of the present study was to quantitatively assess the targeting therapeutic effects against atherosclerosis by establishing a multiscale mathematical model. The multiscale model involved subcellular, cellular and microenvironmental scales to simulate lipid catabolism, macrophage behaviors and dynamics of microenvironmental components, respectively. In vitro and in vivo experimental data were integrated into the mathematical model according to Bayesian statistics, in order to evaluate the therapeutic effects of a proposed NP-based platform for macrophage-specific delivery to simultaneously deliver SR-A siRNA (to reduce LDL uptake) and LXR-L (to stimulate cholesterol efflux). Dosage variation analysis was then performed to investigate the drug efficacy under varied dosage combinations of SR-A siRNA and LXR-L. The simulation results demonstrated that the dynamics of the microenvironmental components presented different developments in Untreated and Treated groups. We also found that the balance of lipid metabolism between uptake and efflux resulted in the improvement of lipid and inflammatory microenvironment, consequently in the plaque regression. In addition, the model predicted optimized dosage combinations according to the co-effect analysis of the two drugs on the lipid microenvironment. This study suggests that multiscale modeling can be a powerful quantitative tool for estimating the therapeutic effects of targeting drugs for plaque regression and designing the enhanced treatment strategies against atherosclerosis.

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.

Physiologically Based Pharmacokinetic (PBPK) Modeling to Predict CYP3A-Mediated Drug Interaction between Saxagliptin and Nicardipine: Bridging Rat-to-Human Extrapolation

The aim of this study was to predict the cytochrome P450 3A (CYP3A)-mediated drug-drug interactions (DDIs) between saxagliptin and nicardipine using a physiologically based pharmacokinetic (PBPK) model. Initially, in silico and in vitro parameters were gathered from experiments or the literature to construct PBPK models for each drug in rats. These models were integrated to predict the DDIs between saxagliptin, metabolized via CYP3A2, and nicardipine, exhibiting CYP3A inhibitory activity. The rat DDI PBPK model was completed by optimizing parameters using experimental rat plasma concentrations after co-administration of both drugs. Following co-administration in Sprague-Dawley rats, saxagliptin plasma concentration significantly increased, resulting in a 2.60-fold rise in AUC, accurately predicted by the rat PBPK model. Subsequently, the workflow of the rat PBPK model was applied to humans, creating a model capable of predicting DDIs between the two drugs in humans. Simulation from the human PBPK model indicated that nicardipine co-administration in humans resulted in a nearly unchanged AUC of saxagliptin, with an approximate 1.05-fold change, indicating no clinically significant changes and revealing a lack of direct translation of animal interaction results to humans. The animal-to-human PBPK model extrapolation used in this study could enhance the reliability of predicting drug interactions in clinical settings where DDI studies are challenging.

Toward improved predictions of pharmacokinetics of transported drugs in hepatic impairment: Insights from the extended clearance model

Hepatic impairment (HI) moderately (<5-fold) affects the systemic exposure (i.e., area under the plasma concentration-time curve [AUC]) of drugs that are substrates of the hepatic sinusoidal organic anion transporting polypeptide (OATP) transporters and are excreted unchanged in the bile and/or urine. However, the effect of HI on their AUC is much greater (>10-fold) for drugs that are also substrates of cytochrome P450 (CYP) 3A enzymes. Using the extended clearance model, through simulations, we identified the ratio of sinusoidal efflux clearance (CL) over the sum of metabolic and biliary CLs as important in predicting the impact of HI on the AUC of dual OATP/CYP3A substrates. Because HI may reduce hepatic CYP3A-mediated CL to a greater extent than biliary efflux CL, the greater the contribution of the former versus the latter, the greater the impact of HI on drug AUC ratio (AUCRHI ). Using physiologically-based pharmacokinetic modeling and simulation, we predicted relatively well the AUCRHI of OATP substrates that are not significantly metabolized (pitavastatin, rosuvastatin, valsartan, and gadoxetic acid). However, there was a trend toward underprediction of the AUCRHI of the dual OATP/CYP3A4 substrates fimasartan and atorvastatin. These predictions improved when the sinusoidal efflux CL of these two drugs was increased in healthy volunteers (i.e., before incorporating the effect of HI), and by modifying the directionality of its modulation by HI (i.e., increase or decrease). To accurately predict the effect of HI on AUC of hepatobiliary cleared drugs it is important to accurately predict all hepatobiliary pathways, including sinusoidal efflux CL.

PBPK Modeling as an Alternative Method of Interspecies Extrapolation that Reduces the Use of Animals: A Systematic Review

INTRODUCTION

Physiologically based pharmacokinetic (PBPK) modeling is a computational approach that simulates the anatomical structure of the studied species and presents the organs and tissues as compartments interconnected by arterial and venous blood flows.

AIM

The aim of this systematic review was to analyze the published articles focused on the development of PBPK models for interspecies extrapolation in the disposition of drugs and health risk assessment, presenting to this modeling an alternative to reduce the use of animals.

METHODS

For this purpose, a systematic search was performed in PubMed using the following search terms: "PBPK" and "Interspecies extrapolation". The revision was performed according to PRISMA guidelines.

RESULTS

In the analysis of the articles, it was found that rats and mice are the most commonly used animal models in the PBPK models; however, most of the physiological and physicochemical information used in the reviewed studies were obtained from previous publications. Additionally, most of the PBPK models were developed to extrapolate pharmacokinetic parameters to humans and the main application of the models was for toxicity testing.

CONCLUSION

PBPK modeling is an alternative that allows the integration of in vitro and in silico data as well as parameters reported in the literature to predict the pharmacokinetics of chemical substances, reducing in large quantity the use of animals that are required in traditional studies.

Phys-DAT: A physiologically-based pharmacokinetic model for unraveling the dissolution, transit and absorption processes using PhysPK®

BACKGROUND AND OBJECTIVE

In silico methods have become the key for efficiently testing and qualifying drug properties. Due to the complexity of the LADME processes and drug characteristics associated to oral drug absorption, there is a growing demand in the development of Physiologically-based Pharmacokinetic (PBPK) software with greater flexibility. Thus, the aims of this work are (i) to develop a mechanistic-based modeling framework of dissolution, transit and absorption (Phys-DAT) processes in the PhysPK platform and (ii) to assess the predictive power of the acausal MOOM methodology embedded in Phys-DAT versus reference ODE-based PBPK software.

METHODS

A PBPK model was developed including unreleased, undissolved and dissolved thermodynamic states of the drug. The gastrointestinal tract (GI) was represented by nine compartments and first-order transit kinetics was assumed for the drug fractions. Dissolution processes were described using solubility-independent or solubility-dependent mechanisms and pH effects. Linear transit and linear absorption mechanisms including gradual decrease absorption rate were considered to represent the passive diffusion process. Internal validation of the Phys-DAT model was performed through simulation-based analysis, considering different theoretical scenarios. External validation was carried out using in silico and in vivo data of GI segments and plasma concentrations. Both BCS I and II class drugs were included.

RESULTS

The model predicts plasma-concentration profiles of each compartment for undissolved, dissolved, and absorbed fractions using PhysPK® v.2.4.1. Internal and external validations demonstrate that the model aligned with the theoretical assumptions and accurately predicted Cmax, Tmax, and AUC 0-t for both BCS I and II drugs. Average Fold Error (AFE), Absolute Average Fold Error (AAFE), and Percent Prediction Error (PPE) calculations indicate good predictive performance, with predicted/observed ratios falling within the acceptable range.

CONCLUSIONS

Phys-DAT represents a mechanistic model for predicting oral absorption, including the dissolution, pH effect, transit, and absorption processes. PhysPK has shown to be a tool with strong prediction accuracy, similar to the obtained by ODE-based PBPK reference software, and the results obtained with the Phys-DAT model for oral administered drugs showed predictive reliability in healthy volunteers, setting the basis to determine the interchangeability of the acausal MOOM methodology with other modeling approaches.

Physiologically Based Pharmacokinetic Modeling of the Drug-Drug Interaction Between CYP3A4 Substrate Glasdegib and Moderate CYP3A4 Inducers in Lieu of a Clinical Study

Glasdegib (DAURISMO) is a hedgehog pathway inhibitor approved for the treatment of acute myeloid leukemia (AML). Cytochrome P450 3A4 (CYP3A4) has been identified as a major metabolism and clearance pathway for glasdegib. The role of CYP3A4 in the clearance of glasdegib has been confirmed with clinical drug-drug interaction (DDI) studies following the coadministration of glasdegib with the strong CYP3A4 inhibitor ketoconazole and the strong inducer rifampin. To evaluate potential drug interactions with CYP3A4 modulators, the coadministration of glasdegib with a moderate CYP3A4 inducer, efavirenz, was evaluated using physiologically based pharmacokinetic (PBPK) modeling using the Simcyp simulator. The glasdegib compound file was developed using measured physicochemical properties, data from human intravenous and oral pharmacokinetics, absorption, distribution, metabolism, and excretion studies, and in vitro reaction phenotyping results. The modeling assumptions, model parameters, and assignments of fractional CYP3A4 metabolism were verified using results from clinical pharmacokinetics (PK) and DDI studies with ketoconazole and rifampin. The verified glasdegib and efavirenz compound files, the latter of which was available in the Simcyp simulator, were used to estimate the potential impact of efavirenz on the PK of glasdegib. PBPK modeling predicted a glasdegib area under the concentration-time curve ratio of 0.45 and maximum plasma concentration ratio of 0.75 following coadministration with efavirenz. The PBPK results, in lieu of a formal clinical study, informed the drug label, with the recommendation to double the clinical dose of glasdegib when administered in conjunction with a moderate CYP3A4 inducer, followed by a resumption of the original dose 7 days post-discontinuation.