Mathematical pharmacokinetics modeling of parenteral depot formulations

A review of existing strategies for designing long-acting parenteral formulations: Focus on underlying mechanisms, and future perspectives

Olanzapine, an atypical antipsychotic was first introduced in 1996 as an oral formulation and is used in the treatment of schizophrenia and bipolar disorder. Recent developments have included parenteral formulations to improve compliance in the treatment of schizophrenia and to treat agitation in patients with schizophrenia and bipolar mania. Olanzapine pamoate long acting injection (depot) is a novel formulation of the atypical antipsychotic olanzapine, which is licensed for the maintenance treatment of schizophrenia. When administered as the pamoate salt, olanzapine has an elimination half-life of approximately 30 days, allowing it to be given at 2- or 4-weekly intervals. An 8-week, randomized, double-blind study in 404 patients acutely ill with schizophrenia demonstrated significant antipsychotic efficacy (versus placebo). A 24-week, randomized, double-blind, active-controlled study in 1,065 schizophrenia patients stabilized with oral olanzapine demonstrated the depot formulation could delay exacerbation of positive symptoms or hospitalization. Apart from local injection reactions and a postinjection delirium/sedation syndrome, no new adverse events additional to those seen with oral olanzapine have been notedto date. The pivotal clinical trials of olanzapine rapid-acting intramuscular injection are reviewed in addition to post-hoc analyses, controlled and naturalistic studies since its launch.

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.

Physiologically-based pharmacokinetic (PBPK) models have become increasingly popular for model-informed drug development (MIDD) over the past decade. While several guidelines for model evaluation exist, these are by design often of a general and non-specific nature. It is clear what steps should be carried out but not necessarily how. For instance, a validation step needs to be performed to check if the predictions of a model indeed match with an external dataset. However, how to quantify this is yet unspecified. In this paper, we propose a more thorough validation approach based on the Continuous Ranked Probability Score (CRPS), a popular metric that explicitly quantifies how well a model recreates the distribution of observed data. Crucially, when applied to PBPK modeling, this metric can be used both insituations where we have individual level predictions and insituations where an entire virtual population is created. The CRPS can also be used to quantify the difference in predictive performance of two competing models. We applied this validation technique to compare two PBPK models. Additionally, we show that using a skill-score approach facilitates the validation of a single model. While our paper focuses on PBPK models, this metric is equally applicable to other models where the goal is to create a virtual population. Additionally, we provide an easily accessible online tool that can be used to perform the proposed validation method.

Model-informed drug development (MIDD) is a framework that utilizes quantitative modeling and simulation methods to integrate nonclinical and clinical data, as well as prior knowledge, to inform drug development and regulatory decision-making. This scoping review examines the current MIDD landscape with a focus on pharmacometrics. It will clarify essential MIDD concepts and provide a taxonomy of terms. Additionally, it will evaluate the implications of the International Council for Harmonisation (ICH) M15 MIDD draft guidelines and summarize case studies that illustrate the use of MIDD in various drug development and lifecycle management scenarios. The diverse case studies span various therapeutic areas, small molecules, monoclonal antibodies, and long-acting injectable dosage forms. The ICH M15 MIDD guidelines aim to align the expectations of regulators and sponsors, support consistent regulatory decisions, and minimize errors in the acceptance of modeling and simulation. MIDD is a transformative and rigorous process that fosters collaboration between industry and regulatory agencies during drug development.MIDD has enabled accelerated approvals of drugs for pediatric conditions and rare diseases, where it can be difficult to recruit enough patients for efficacy studies. MIDD has contributed to the clinical pharmacology strategies that have successfully allowed dose extrapolation to related disease indications, dosage forms, and clinical populations without additional clinical trials.

The conventional strategy of prescribing the same dosage to all patients can result in suboptimal efficacy and safety. This is particularly true when considering drug-gene interactions (DGIs), drug-drug interactions (DDIs), or in individuals with compromised organ function. Precision medicine, which aims to tailor drug regimens based on individual patient characteristics, offers a promising alternative by focusing on drug disposition, efficacy, and safety. However, clinical trials face ethical and practical challenges and cannot cover all real-world patient scenarios. Thus, physiological based pharmacokinetic (PBPK) modeling offers a unique framework for enhancing model-informed drug development (MIDD) and precision dosing (MIPD). Despite this, most PBPK applications primarily assess drug pharmacokinetics without evaluating efficacy or safety outcomes. This limits the full potential of mechanistic models. In this study we used integrated PBPK, Quantitative Systems Pharmacology (QSP), and toxicology models to predict risks in scenarios like DGIs, DDIs, and varied renal impairment by simultaneously assessing drug PK, pharmacological effect, and toxicity. The findings underscore the importance of considering pharmacological effects and myotoxicity risks, which differed from changes seen in plasma exposure. This study demonstrates the value of PBPK-QSP models in guiding dose adjustments to optimize the efficacy and safety balance in target patient populations, showcasing their strength in MIDD and MIPD strategies.

Warfarin, a commonly prescribed oral anticoagulant medication, is highly effective in treating deep vein thrombosis and pulmonary embolism. However, the clinical dosing of warfarin is complicated by high interindividual variability in drug exposure and response and its narrow therapeutic index. CYP2C9 genetic polymorphism and drug-drug interactions (DDIs) are substantial contributors to this high variability of warfarin pharmacokinetics (PK), among numerous factors. Building a physiology-based pharmacokinetic (PBPK) model for warfarin is not only critical for a mechanistic characterization of warfarin PK but also useful for investigating the complicated dose-exposure relationship of warfarin. Thus, the objective of this study was to develop a PBPK model for warfarin that integrates information regarding CYP2C9 genetic polymorphisms and their impact on DDIs. Generic PBPK models for both S- and R-warfarin, the two enantiomers of warfarin, were constructed in R with the mrgsolve package. As expected, a generic PBPK model structure did not adequately characterize the warfarin PK profile collected up to 15 days following the administration of a single oral dose of warfarin, especially for S-warfarin. However, following the integration of an empirical target-mediated drug disposition (TMDD) component, the PBPK-TMDD model well characterized the PK profiles collected for both S- and R-warfarin in subjects with different CYP2C9 genotypes. Following the integration of enzyme inhibition and induction effects, the PBPK-TMDD model also characterized the PK profiles of both S- and R-warfarin in various DDI settings. The developed mathematic framework may be useful in building algorithms to better inform the clinical dosing of warfarin. SIGNIFICANCE STATEMENT: The present study found that a traditional physiology-based pharmacokinetic (PBPK) model cannot sufficiently characterize the pharmacokinetic profiles of warfarin enantiomers when warfarin is administered as a single dose, but a PBPK model with a target-mediated drug disposition mechanism can. After incorporating CYP2C9 genotypes and drug-drug interaction information, the developed model is anticipated to facilitate the understanding of warfarin disposition in subjects with different CYP2C9 genotypes in the absence and presence of both cytochrome P450 inhibitors and cytochrome P450 inducers.

This paper presents a case study demonstrating the application of model-informed drug development (MIDD) and early modeling integration in the development of a sustained release (SR) formulation of flucytosine for cryptococcal meningoencephalitis (CM) in HIV-infected patients. The study aimed to showcase the value of physiologically based pharmacokinetic (PBPK) modeling and physiologically based biopharmaceutics modeling (PBBM) in guiding decisions and optimizing therapeutic strategies throughout drug development. The MIDD strategy started with a PBPK model based on limited literature data, iteratively refined informed by data from two Phase 1 clinical studies with various flucytosine formulations under different prandial conditions in healthy participants, enhancing model reliability. The PBPK/PBBM model played a substantial role in guiding SR prototype formulation design, dose selection for studies in healthy participants, and dosage determination for an upcoming Phase 2 clinical study in patients, with a focus on low-weight patients. The flexibility of MIDD allowed quick assessments of ancillary questions during the program. Ad hoc simulations evaluated strategies such as adding a loading dose for SR treatment and assessing drug exposure in unconscious patients, contributing to optimized therapeutic approaches. In conclusion, this case study emphasizes the benefits of MIDD and early model integration in drug development. PBPK/PBBM modeling facilitated informed decisions, leading to successful design and dosing of an SR flucytosine formulation for CM treatment. Continuous knowledge integration within MIDD ensured model adaptability and reliability, demonstrating its value in addressing evolving challenges and optimizing therapeutic outcomes.

Decision Science for Generic Drug Development and Review.

Doxycycline hyclate (CAS 24390-14-5, doxycycline-h), an antibacterial with time-dependent action, was formulated as a non-irritating long-acting parenteral formulation based on a beta-cyclodextrin: poloxamer-based matrix (doxycycline-h-LA). Tissue and serum concentrations vs time profile were investigated after its subcutaneous injection to Wistar rats. Serum concentration profiles and key pharmacokinetic (PK) variables of doxycycline-h-LA were compared to the corresponding profiles and PK values obtained with an aqueous formulation of doxycycline-h administered either intramuscularly, orally or intravenously to Wistar rats. In all groups, the dose was 10 mg/kg. Doxycycline-h-LA showed outstanding bioavailability (951% or 477% if a correction formula is considered), as compared to the one obtained with an aqueous formulation (106-82%, respectively). Corresponding values for maximum serum concentration were 3.19 microg/ml and 3.00 microg/ml, respectively, and elimination half-lives were completely different: 42.49 h and 2.77 h for doxycycline-h-LA and the aqueous formulation, respectively. Considering minimal inhibitory concentrations of doxycycline for sensitive and resistant bacteria (from < or = 0.5 to > or =1.5 microg/ml), doxycycline-h-LA could be injected every 2 or 3 days, while aqueous doxycycline-h would require a dosing interval from 7.5 to 11 h. But if tissue concentrations are taken as braking points, the dosing interval will vary from 48 to 94 h. For doxycycline-h-LA, mean tissue:serum ratios were 2:1 for lungs, 9.8:1 for kidneys and 2.2:1 for intestine homogenates. These values are in close agreement with those found for the distribution of doxycycline in other species.

Purpose:Recently, there has been rapid development in model-informed drug development, which has the potential to reduce animal experiments and accelerate drug discovery. Physiologically based pharmacokinetic (PBPK) and machine learning (ML) models are commonly used in early drug discovery to predict drug properties. However, basic PBPK models require a large number of molecule-specific inputs from in vitro experiments, which hinders the efficiency and accuracy of these models. To address this issue, this paper introduces a new computational platform that combines ML and PBPK models. The platform predicts molecule PK profiles with high accuracy and without the need for experimental data.Methods:This study developed a whole-body PBPK model and ML models of plasma protein fraction unbound (fup), Caco-2 cell permeability, and total plasma clearance to predict the PK of small molecules after intravenous administration. Pharmacokinetic profiles were simulated using a “bottom-up” PBPK modeling approach with ML inputs. Additionally, 40 compounds were used to evaluate the platform’s accuracy.Results:Results showed that the ML-PBPK model predicted the area under the concentration-time curve (AUC) with 65.0% accuracy within a 2-fold range, which was higher than using in vitro inputs with 47.5% accuracy.Conclusion:The ML-PBPK model platform provides high accuracy in prediction and reduces the number of experiments and time required compared to traditional PBPK approaches. The platform successfully predicts human PK parameters without in vitro and in vivo experiments and can potentially guide early drug discovery and development.

Whole-Body Disposition and Physiologically Based Pharmacokinetic Modeling of Adeno-Associated Viruses and the Transgene Product

Several regulatory guidances on the use of physiologically based pharmacokinetic (PBPK) analyses and physiologically based biopharmaceutics model(s) (PBBM(s)) have been issued. Workshops are routinely held, demonstrating substantial interest in applying these modeling approaches to address scientific questions in drug development. PBPK models and PBBMs have remarkably contributed to model-informed drug development (MIDD) such as anticipating clinical PK outcomes affected by extrinsic and intrinsic factors in general and specific populations. In this review, we proposed practical considerations for a "base" PBPK model construction and development, summarized current status, challenges including model validation and gaps in system models, and future perspectives in PBPK evaluation to assess a) drug metabolizing enzyme(s)- or drug transporter(s)- mediated drug-drug interactions b) dosing regimen prediction, sampling timepoint selection and dose validation in pediatric patients from newborns to adolescents, c) drug exposure in patients with renal and/or and hepatic organ impairment, d) maternal-fetal drug disposition during pregnancy, and e) pH-mediated drug-drug interactions in patients treated with proton pump inhibitors/acid-reducing agents (PPIs/ARAs) intended for gastric protection. Since PBPK can simulate outcomes in clinical studies with enrollment challenges or ethical issues, the impact of PBPK models on waivers and how to strengthen study waiver is discussed.

GSK2838232 (GSK8232) is a second-generation maturation inhibitor (MI) developed for the treatment of HIV with excellent broad-spectrum virological profiles. The compound has demonstrated promising clinical results as an orally administered agent. Additionally, the compound’s physical and pharmacological properties present opportunities for exploitation as long-acting parenteral formulations. Despite unique design constraints including solubility and dose of GSK8232, we report on three effective tunable drug delivery strategies: active pharmaceutical ingredient (API) suspensions, ionic liquids, and subdermal implants. Promising sustained drug release profiles were achieved in rats with each approach. Additionally, we were able to tune drug release rates through a combination of passive and active strategies, broadening applicability of these formulation approaches beyond GSK8232. Taken together, this report is an important first step to advance long-acting formulation development for critical HIV medicines that do not fit the traditional profile of suitable long-acting candidates.

In vitro - in vivo relations for the parenteral liposomal formulation of Amphotericin B: A clinically relevant approach with PBPK modeling