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This review paper discusses the key aspects of ocular biopharmaceutics, with emphasis on the crucial role played by ocular compartmental modelling and simulation in deciphering physiological conditions related to various eye diseases. It describes eye’s intricate structure and function and the need for precise and targeted drug delivery systems to address prevalent eye conditions. The review categorizes and discusses various formulations employed in ocular drug delivery, delineating their respective advantages and limitations. Additionally, it probes the challenges inherent in diverse routes of drug administration for ocular therapies and provides insights into the complexities of achieving optimal drug concentrations at the target site within the eye. The central theme of this work is the ocular compartmental modelling and simulations. Hence, this works discusses on the nuanced understanding of physiological conditions within the eye, drug distribution, drug release kinetics, and key considerations for ocular compartmental modelling and simulations. By combining information from various sources, this review aims to serve as a comprehensive reference for researchers, clinicians, and pharmaceutical developers. It covers the multifaceted landscape of ocular biopharmaceutics and the transformative impact of modelling and simulation in optimizing ocular drug delivery strategies. Graphical Abstract

Physiologically‐based pharmacokinetic (PBPK) modeling can support decision‐making on maternal medication use during breastfeeding. This study aimed to enhance lactation PBPK models in two ways. First, the utility of integrating permeability‐ versus perfusion‐limited distribution to human milk was explored using the Simcyp Simulator. Secondly, for permeability‐limited models, drug‐specific bidirectional intrinsic clearance across the blood‐milk barrier, predicted from drug physicochemical properties, was incorporated into lactation PBPK models. Initially, reference PBPK models were developed and verified against published clinical data. Geometric Mean Fold Error (GMFE; ~accuracy) and Average Fold Error (AFE; ~bias) for these models ranged from 1.13–1.51 and 0.68–1.42, respectively. These verified models were then extended to lactation PBPK models applying either permeability‐ or perfusion‐limited assumptions for drug distribution across the blood‐milk barrier. The lactation PBPK models were applied to predict drug concentrations in human milk and relative infant doses (RID) for 11 small molecule drugs with diverse physicochemical and disposition profiles. The models successfully predicted observed plasma PK, human milk concentration‐time profiles, and milk‐to‐plasma ratios. Nine drugs had RID values below the safety threshold of 25%, while levetiracetam and nevirapine showed relatively higher RIDs (up to 21%). Based on these findings, a decision tree is proposed to guide the selection between permeability‐ or perfusion‐limited distribution models in future lactation PBPK applications using Simcyp. This workflow can be extended beyond the 11 model drugs evaluated, supporting broader infant risk assessment for maternal medication during lactation.

Vinblastine (VBL) is a vinca alkaloid‐class cytotoxic chemotherapeutic that causes microtubule disruption and is typically used to treat hematologic malignancies. VBL is characterized by a narrow therapeutic index, with key dose‐limiting toxicities being myelosuppression and neurotoxicity. Pharmacokinetics (PK) of VBL is primarily driven by ABCB1‐mediated efflux and CYP3A4 metabolism, creating potential for drug–drug interaction. To characterize sources of variability in VBL PK, we developed a physiologically based pharmacokinetic (PBPK) model in Mdr1a/b(−/−) knockout and wild‐type mice by incorporating key drivers of PK, including ABCB1 efflux, CYP3A4 metabolism, and tissue‐specific tubulin binding, and scaled this model to accurately simulate VBL PK in humans and pet dogs. To investigate the capability of the model to capture interindividual variability in clinical data, virtual populations of humans and pet dogs were generated through Monte Carlo simulation of physiologic and biochemical parameters and compared to the clinical PK data. This model provides a foundation for predictive modeling of VBL PK. The base PBPK model can be further improved with supplemental experimental data identifying drug–drug interactions, ABCB1 polymorphisms and expression, and other sources of physiologic or metabolic variability. Schematic of Vinblastine PBPK Model.

Dosimetry models for the estimation of particle deposition in the human respiratory tract (RT) in conjunction with clearance transport models are vital components to relate human exposure with internal dose in a quantitative manner. The current work highlights knowledge and modelling approaches on particle deposition and translocation in the human body in an effort to determine health risks in respect to different particle physicochemical properties and human physiology parameters. These include breathing conditions, variability of the geometry of the RT, chemical composition and size of deposits. Different dosimetry modelling approaches have been studied including empirical formulations, one-dimensional flow modelling and computational fluid dynamic methods (CFD). The importance of a realistic modelling of hygroscopicity has been also investigated. A better understanding of the relationship between health effects and inhaled particle dose may be elaborated using dosimetry and clearance modelling tools. A future required approach is to combine dosimetry models with physiologically based pharmacokinetic models (PBPK) to simulate the transport and cumulative dose of particle-bound chemical species in different organs and tissues of the human body.

Bile salts are biosurfactants released into the intestinal lumen which play an important role in the solubilisation of fats and certain drugs. Their concentrations vary along the gastrointestinal tract (GIT). This is significant for implementation in physiologically based pharmacokinetic (PBPK) modelling to mechanistically capture drug absorption. The aims of this meta-analysis were to collate all appropriate data on intestinal bile salt concentrations in healthy adults across all GIT segments in fasted and fed states for the purpose of PBPK modelling. Terms relating to bile composition were searched in PubMed and Google Scholar from inception to May 2024. Selected studies included aspirated intestinal fluid collected via gastric tubes or colonoscopy. Results showed high variability across studies and a time-dependency for the fed state. Data were rich for the duodenum, which showed a two-fold increase for the fed state versus the fasted state within multiple studies. Peaks and troughs in bile salt concentrations along the GIT were observed for both fasted and fed states, likely due to segmental water absorption differences. The highest between subject variability was observed for the duodenum in the fasted and fed state and the fed proximal jejunum, distal ileum, and colon. The findings from this meta-analysis can be used for the purpose of PBPK modelling to capture segmental drug solubilisation and absorption in fasted and fed states. However, data are lacking under different fed conditions, especially following low-fat meals, so the impact of different fat content associated with different meals on bile salt concentrations cannot be discerned. Graphical Abstract Gastrointestinal bile salt concentrations in healthy subjects A meta-analysis has been conducted to collate fasted and fed gastrointestinal bile salt concentrations in healthy subjects for the purpose of physiologically-based pharmacokinetic (PBPK) modelling within the Simcyp and other PBPK simulators. Values are presented as weighted means with coefficient of variability for each segment. These data will help improve mechanistic models of oral drug absorption.

An important aspect in the development of small molecules as drugs or agrochemicals is their systemic availability after intravenous and oral administration. The prediction of the systemic availability from the chemical structure of a potential candidate is highly desirable, as it allows to focus the drug or agrochemical development on compounds with a favorable kinetic profile. However, such predictions are challenging as the availability is the result of the complex interplay between molecular properties, biology and physiology and training data is rare. In this work we improve the hybrid model developed earlier (Schneckener in J Chem Inf Model 59:4893–4905, 2019). We reduce the median fold change error for the total oral exposure from 2.85 to 2.35 and for intravenous administration from 1.95 to 1.62. This is achieved by training on a larger data set, improving the neural network architecture as well as the parametrization of mechanistic model. Further, we extend our approach to predict additional endpoints and to handle different covariates, like sex and dosage form. In contrast to a pure machine learning model, our model is able to predict new end points on which it has not been trained. We demonstrate this feature by predicting the exposure over the first 24 h, while the model has only been trained on the total exposure.

Seo-Yeon Choi,1 Eunsol Yang,2 Kwang-Hee Shin1,3 1College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook National University, Daegu, Republic of Korea; 2Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA; 3Infectious Disease Healthcare, Kyungpook National University, Daegu, Republic of KoreaCorrespondence: Kwang-Hee Shin, College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea, Tel +82 53 950 8582, Fax +82 53 950 8557, Email kshin@knu.ac.krBackground: Pregnancy-induced physiological changes can substantially alter venlafaxine pharmacokinetics. Despite the clinical relevance of both venlafaxine and its active metabolite, O-desmethylvenlafaxine (ODV), no physiologically-based pharmacokinetic (PBPK) models have been developed that simultaneously describe their disposition during pregnancy. In this study a PBPK model was developed to predict maternal and fetal exposure to venlafaxine and ODV and to optimize dosing regimens.Methods: PBPK models for venlafaxine and ODV in non-pregnant women, pregnant women, and the fetal–placental unit were developed using the Simcyp® simulator. Model performance was evaluated using visual predictive checks, assessing whether observed data were contained within the predicted 95% confidence intervals, and by comparing predicted versus observed ratios for maximum plasma concentration (Cmax) and area under the concentration–time curve (AUC) using a prespecified range (0.7– 1.3).Results: In non-pregnant women, observed venlafaxine and ODV concentrations fell within the 95% confidence intervals of model predictions, with Cmax and AUC prediction ratios between 0.7 and 1.3. Most observed data in pregnant women also fell within the 95% confidence intervals. Venlafaxine and ODV concentrations decreased as pregnancy progressed for doses ranging from 37.5 to 225 mg. Cord-to-maternal concentration ratios were approximately 1.02 at 37.5– 150 mg and 1.01 at 225 mg. Predicted venlafaxine and ODV concentrations remained within the therapeutic range (100– 400 ng/mL) at 150 mg during the first and second trimesters, whereas 225 mg was necessary in the third trimester. At a 375 mg dose, the umbilical cord Cmax for venlafaxine reached 195.26 ng/mL, a level approaching thresholds of fetal toxicity. These findings should be interpreted with caution, given the limited sample size in pregnant women (n= 7 for plasma and n=9 for cord blood).Conclusion: A venlafaxine dose of 150 mg/day is recommended during pregnancy, balancing efficacy with the risk of toxicity in both mother and fetus. Keywords: venlafaxine, O-desmethylvenlafaxine, pregnancy, PBPK modeling

Background: Following vitamin D3 oral administration, attained serum concentrations of its metabolite 25-hydroxyvitamin D3 (25(OH)D3) are variable among children. Methods: We developed physiologically based pharmacokinetic (PBPK) modelling using annually measured serum 25(OH)D3 concentrations in 77 Cape Town schoolchildren aged 6–11 years who received weekly oral doses of 10,000 IU vitamin D3 for 3 years during a clinical trial (Δ25(OH)D = 32.2 nmol/L, 95% CI: [−3.2, 65.8] nmol/L). Simulations were performed to test the model on 463 other participants in the same trial, and in a cohort of 1756 Mongolian schoolchildren aged 6–11 years who received weekly oral doses of 14,000 IU vitamin D3 for 3 years in another trial. Results: The best model attributed most of the variability in post-supplementation 25(OH)D3 concentrations to hepatic clearance and covariates including weight (ΔAIC = −21) and ZBMI (body mass index Z-score, ΔAIC = −34). For 463 other children from the Cape Town trial (Δ25(OH)D = 25.8 nmol/L, 95% CI: [8.3, 47.2] nmol/L), mean estimation error was 5.3 nmol/L, and 76.7% of observations were within the 95% prediction intervals. Our simulation supported the previous proposal that serum 25(OH)D3 should exceed 50 nmol/L among 97.5% of European children at 24.4 μg/day vitamin D3 dosing. At a higher weekly dose (14,000 IU), the Mongolian children demonstrated a higher average increase in serum 25(OH)D3 (40.6 [−2.9, 88.9] nmol/L) but were overestimated by the model. Conclusion: We developed the first PBPK model to successfully predict the long-term serum 25(OH)D3 increases in healthy schoolchildren in Cape Town who received orally administered vitamin D3 and exhibited higher relative increases than Mongolian children.

For successful oral drug development, defining a bioequivalence (BE) safe space is critical for the identification of newer bioequivalent formulations or for setting of clinically relevant in vitro specifications to ensure drug product quality. By definition, the safe space delineates the dissolution profile boundaries or other drug product quality attributes, within which the drug product variants are anticipated to be bioequivalent. Defining a BE safe space with physiologically based biopharmaceutics model (PBBM) allows the establishment of mechanistic in vitro and in vivo relationships (IVIVR) to better understand absorption mechanism and critical bioavailability attributes (CBA). Detailed case studies on how to use PBBM to establish a BE safe space for both innovator and generic drugs are described. New case studies and literature examples demonstrate BE safe space applications such as how to set in vitro dissolution/particle size distribution (PSD) specifications, widen dissolution specification to supersede f2 tests, or application toward a scale-up and post-approval changes (SUPAC) biowaiver. A workflow for detailed PBBM set-up and common clinical study data requirements to establish the safe space and knowledge space are discussed. Approaches to model in vitro dissolution profiles i.e. the diffusion layer model (DLM), Takano and Johnson models or the fitted PSD and Weibull function are described with a decision tree. The conduct of parameter sensitivity analyses on kinetic dissolution parameters for safe space and virtual bioequivalence (VBE) modeling for innovator and generic drugs are shared. The necessity for biopredictive dissolution method development and challenges with PBBM development and acceptance criteria are described.

During drug development, the design, interpretation and risk assessment of drug-drug interaction (DDI) are generally performed with physiologically-based pharmacokinetic (PBPK) modelling. Critical parameters are the hepatic metabolic fraction (fm) and intestinal availability (F G ) which are commonly informed by clinical data. In this study, two methods for the simultaneous estimation of these parameters are proposed which utilize the distinctive changes in substrate’s plasma concentration profiles in response to inhibition of intestinal and hepatic enzymes. The two-dimensional DDI (2D-DDI) method estimates the fm and F G values directly from the ratios of area-under-curve (AUCR) and maximum concentration (C max R), while the population PBPK method utilizes the full concentration–time data of a substrate without or with an inhibitor. The utility of both methods was demonstrated for a broad range of > 50,000 virtual and six actual CYP3A substrates. The 2D-DDI method is fast, reliable, and does not require a priori PBPK model development. The population PBPK method can estimate the population parameters and inter-individual variabilities of fm and F G and is applicable to more complex DDIs (e.g., multiple pathways/dynamic inhibitor concentration–time profiles) without the need for IV data. Like other approaches, both methods show an increasing uncertainty for substrates with high hepatic extraction and sensitivity to the assumed degree of enzyme inhibition. While both methods were evaluated for CYP3A substrates, the methodology equally applies to other enzymes. Additionally, this study provides guidance for clinical DDI study design to facilitate robust DDI extrapolation necessary to inform drug labels on concomitant medications in lieu of clinical trials. Graphical Abstract