In 2017, the free and open-source software Open Systems Pharmacology (OSP) was launched. Since then, OSP has evolved from a small community into a diverse network of stakeholders committed to advancing open-source solutions for model-informed drug development (MIDD). In this context, the first OSP Community Conference was hosted by Novartis in Basel, Switzerland, on October 7-8, 2024, which gathered over 100 attendees from more than 40 institutions. This perspective synthesizes key insights from the conference.

Physiologically Based Biopharmaceutics Modeling (PBBM) links in vitro solubility and dissolution kinetics of oral drugs to Physiologically Based Pharmacokinetic (PBPK) models, enabling the prediction of drug bioavailability. This approach reduces reliance on animal experimentation during drug development. Part 1 of this series introduces a novel, open-source PBBM workflow using the Open Systems Pharmacology (OSP) Software Suite. It combines the newly developed OSP solubility toolbox to estimate drug aqueous solubility and bile salt micelle partitioning from in vitro data, with an updated MoBi® dissolution model that accounts for factors such as hydrodynamic diffusion layer thickness changes and micelle partitioning. This approach was applied to poorly soluble, highly permeable vericiguat, a first-in-class soluble guanylate cyclase (sGC) stimulator for the treatment of chronic heart failure. The solubility models effectively described the aqueous solubility-pH profile and bile salt partitioning, while the dissolution model captured the in vitro dissolution kinetics of various tablet formulations. Part 2 focuses on integrating developed PBBM sub-models into PBPK models using PK-Sim® to predict vericiguat's clinical pharmacokinetics after oral administration. This PBBM workflow demonstrates potential for diverse applications, including predictive bioavailability and bioequivalence assessments, in silico formulation optimization, formulation bridging, dose selection, and setting dissolution specifications.

To establish bioequivalence (BE) of a generic test formulation with respect to a reference listed drug, it is necessary to demonstrate a comparable rate and extent to which active ingredients reach the site of action. To decrease unnecessary human testing and simulate scenarios involving specific populations or challenges with recruitment or study design, industry and regulators are increasingly considering in silico virtual bioequivalence (VBE) approaches. This tutorial introduces the VBEToolbox R package: a toolbox within the Open Systems Pharmacology framework to streamline and standardize computational VBE workflows. The package integrates in vitro and in vivo data to train pharmacokinetic models through inference of inter-individual variability from clinical data and establishment of in vitro to in vivo extrapolations. A nonparametric approach is adopted to account for uncertainties from parameter non-identifiability. The trained model is then applied to determine the study size with statistical power needed to demonstrate BE virtually. The use of the VBE tool is illustrated with two case studies. The first evaluates the VBE of petrolatum and ethylene glycol dermal formulations of testosterone by integrating in vitro skin permeation tests, vehicle/skin partitioning data, testosterone solubility data, and in vivo absorption data in a mechanistic in vitro/in vivo dermal absorption model. The second assesses the VBE of two oral bupropion formulations by integrating in vitro dissolution data in a physiologically based pharmacokinetic model. These case studies highlight essential considerations for model development, training, and extrapolation toward application for VBE assessment.

This manuscript introduces a novel, free and open-source physiologically based biopharmaceutics modeling (PBBM) workflow to support decision-making during oral drug product development. Developed within the Open Systems Pharmacology (OSP) framework, this workflow combines three open-source tools to predict in vivo absorption by integrating solubility, dissolution, and systemic pharmacokinetics. The PBBM workflow was successfully applied by 1) deriving solubility and dissolution parameters for vericiguat from in vitro experiments as detailed in the accompanying Part 1 publication (Vrenken et al., 2025); 2) then integrating these parameters into a physiologically based pharmacokinetic (PBPK) model. This model incorporates updates to luminal parameters, including pH and bile salt concentration, and employs a new dissolution model accounting for bile salt effects and hydrodynamics. Simulations of vericiguat pharmacokinetics in fasted, fed, and proton pump inhibitor (PPI)-treated states showed good agreement with observed clinical data. Further refinements that could enhance the PBBM workflow's capabilities were identified. However, the successful application to vericiguat highlights its potential for PBBM applications, including virtual bioequivalence assessments, formulation bridging, and dose selection. While validation across additional case studies is necessary, this free and open-source approach provides a robust foundation for advancing PBBM.

Conducting clinical studies on drug-drug-gene interactions (DDGIs) and extrapolating the findings into clinical dose recommendations is challenging due to the high complexity of these interactions. Here, physiologically-based pharmacokinetic (PBPK) modeling networks present a new avenue for exploring such complex scenarios, potentially informing clinical guidelines and handling patient-specific DDGIs at the bedside. Moreover, they provide an established framework for drug-drug interaction (DDI) submissions to regulatory agencies. The cytochrome P450 (CYP) 2D6 enzyme is particularly prone to DDGIs due to the high prevalence of genetic variation and common use of CYP2D6 inhibiting drugs. In this study, we present a comprehensive PBPK network covering CYP2D6 drug-gene interactions (DGIs), DDIs, and DDGIs. The network covers sensitive and moderate sensitive substrates, and strong and weak inhibitors of CYP2D6 according to the United States Food and Drug Administration (FDA) guidance. For the analyzed CYP2D6 substrates and inhibitors, DD(G)Is mediated by CYP3A4 and P-glycoprotein were included. Overall, the network comprises 23 compounds and was developed based on 30 DGI, 45 DDI, and seven DDGI studies, covering 32 unique drug combinations. Good predictive performance was demonstrated for all interaction types, as reflected in mean geometric mean fold errors of 1.40, 1.38, and 1.56 for the DD(G)I area under the curve ratios as well as 1.29, 1.43, and 1.60 for DD(G)I maximum plasma concentration ratios. Finally, the presented network was utilized to calculate dose adaptations for CYP2D6 substrates atomoxetine (sensitive) and metoprolol (moderate sensitive) for clinically untested DDGI scenarios, showcasing a potential clinical application of DDGI model networks in the field of model-informed precision dosing.

The Open Systems Pharmacology Suite contains different software tools with its main components being PK-Sim® and MoBi®. PK-Sim® includes the most relevant and “standardized” approaches for physiologically based pharmacokinetic modeling, enabling the simulation of drug concentrations in preclinical species as well as in human. One important part of the predefined model structure in PK-Sim® is the sub-model for gastrointestinal transit and absorption. This chapter discusses a selection of the most relevant physiological parameters important for the oral administration of modified-release formulations, focusing especially on those parameters defining the intestinal permeability and the active processes that can be included in the model. The dissolved form assumes that the drug is administered as a solution and that the drug is already completely dissolved. The zero and first-order dissolution functions are simple empirical equations that define the corresponding release kinetics. The particle dissolution formulation is a mechanistic description of the dissolution processes available in PKSim®.

The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim ® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim ® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim ® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios.

The objective of this study was to develop a physiologically based pharmacokinetic (PBPK) model for amoxicillin for non-pregnant, pregnant and postpartum populations by compiling a database incorporating reported changes in the anatomy and physiology throughout the postpartum period. A systematic literature search was conducted to collect data on anatomical and physiological changes in postpartum women. Empirical functions were generated describing the observed changes providing the basis for a generic PBPK framework. The fraction unbound ( $${f}_{u}$$ ) of predominantly albumin-bound drugs was predicted in postpartum women and compared with experimentally observed values. Finally, a specific amoxicillin PBPK model was newly developed, verified for non-pregnant populations and translated into the third trimester of pregnancy (29.4–36.9 gestational weeks) and early postpartum period (drug administration 1.5–3.8 h after delivery). Pharmacokinetic predictions were evaluated using published clinical data. The literature search yielded 105 studies with 1092 anatomical and physiological data values on 3742 postpartum women which were used to generate various functions describing the observed trends. The $${f}_{u}$$ could be adequately scaled to postpartum women. The pregnancy PBPK model predicted amoxicillin disposition adequately as did the postpartum PBPK model, although clearance was somewhat underestimated. While more research is needed to establish fully verified postpartum PBPK models, this study provides a repository of anatomical and physiological changes in postpartum women that can be applied to future modeling efforts. Ultimately, structural refinement of the developed postpartum PBPK model could be used to investigate drug transfer to the neonate via breast-feeding in silico.

Systems pharmacology integrates structural biological and pharmacological knowledge and experimental data, enabling dissection of organism and drug properties and providing excellent predictivity. The development of systems pharmacology models is a significant task requiring massive amounts of background information beyond individual trial data. The qualification of models needs repetitive demonstration of successful predictions. Open Systems Pharmacology is a community that develops, qualifies, and shares professional open source software tools and models in a collaborative open-science way.

Food and Drug Administration submissions of physiologically based pharmacokinetic (PBPK) modeling and simulation of small-molecule drugs document the relevance of pediatric drug development and, in particular, information on dosing strategies in children. The most relevant prerequisite for reliable PBPK-based translation of adult pharmacokinetics of a small molecule to children is knowledge of the drug-specific absorption, distribution, metabolism, and elimination (ADME) processes in adults together with existing information about ontogeny of ADME processes relevant for the drug. All mechanisms driving a drug's clearance are of specific importance. For other drug modalities, our knowledge of ADME processes and ontogeny is still limited. More research is required, for example, to understand why some therapeutic proteins show complex differences in pharmacokinetics between adults and children, whereas other proteins seem to follow simple allometric scaling rules. Ontogeny information originates from various sources, such as (semi)quantitative mRNA expression, in vitro activity data, and deconvolution of in vivo pharmacokinetic data. The workflow for pediatric predictions is well described in several articles documenting successful translation from adults to children. The technical hurdles for PBPK modeling are low. State-of-the-art PBPK modeling software tools provide integrated pediatric translation workflows. For example, PK-Sim and MoBi are freely available as fully transparent open-source software via Open Systems Pharmacology (OSP). With the latest 2019 software release, version 8.0, OSP even provides a fully integrated technical framework for the qualification (and requalification) of any specific intended PBPK use in line with Food and Drug Administration and European Medicines Agency PBPK guidance. Qualification packages for pediatric translation are available on the OSP platform.

Proteins are an increasingly important class of drugs used as therapeutic as well as diagnostic agents. A generic physiologically based pharmacokinetic (PBPK) model was developed in order to represent at whole body level the fundamental mechanisms driving the distribution and clearance of large molecules like therapeutic proteins. The model was built as an extension of the PK-Sim model for small molecules incorporating (i) the two-pore formalism for drug extravasation from blood plasma to interstitial space, (ii) lymph flow, (iii) endosomal clearance and (iv) protection from endosomal clearance by neonatal Fc receptor (FcRn) mediated recycling as especially relevant for antibodies. For model development and evaluation, PK data was used for compounds with a wide range of solute radii. The model supports the integration of knowledge gained during all development phases of therapeutic proteins, enables translation from pre-clinical species to human and allows predictions of tissue concentration profiles which are of relevance for the analysis of on-target pharmacodynamic effects as well as off-target toxicity. The current implementation of the model replaces the generic protein PBPK model available in PK-Sim since version 4.2 and becomes part of the Open Systems Pharmacology Suite.

This tutorial presents the workflow of adapting an adult physiologically based pharmacokinetic (PBPK) model to the pregnant populations using the Open Systems Pharmacology (OSP) software suite (www.open-systems-pharmacology.org). This workflow is illustrated using a previously published PBPK model for metronidazole that is extrapolated to pregnancy by parameterizing and extending the model structure in terms of pregnancy-induced physiological changes. Importantly, this workflow can be applied to other scenarios where PBPK models need to be re-parameterized or structurally modified.

Methods: The system parameter model basis was consolidated from literature including information on general physiology [1] as well as on specific elimination [2] and metabolic pathways and implemented in the Open Systems Pharmacology Suite (PK-Sim/MoBi, [3], www.opensystems-pharmacology.org). Substance models were developed based on physico-chemical and clinical pharmacokinetic literature data for nonpregnant woman and predictions for pregnant populations were evaluated against pharmacokinetic literature data collected during pregnancy.

Background Since pregnant women are considerably underrepresented in clinical trials, information on optimal dosing in pregnancy is widely lacking. Physiologically based pharmacokinetic (PBPK) modeling may provide a method for predicting pharmacokinetic changes in pregnancy to guide subsequent in vivo pharmacokinetic trials in pregnant women, minimizing associated risks. Objectives The goal of this study was to build and verify a population PBPK model that predicts the maternal pharmacokinetics of three predominantly renally cleared drugs (namely cefazolin, cefuroxime, and cefradine) at different stages of pregnancy. It was further evaluated whether the fraction unbound ( f u ) could be estimated in pregnant women using a proposed scaling approach. Methods Based on a recent literature review on anatomical and physiological changes during pregnancy, a pregnancy population PBPK model was built using the software PK-Sim ® /MoBi ® . This model comprised 27 compartments, including nine pregnancy-specific compartments. The PBPK model was verified by comparing the predicted maternal pharmacokinetics of cefazolin, cefuroxime, and cefradine with observed in vivo data taken from the literature. The proposed scaling approach for estimating the f u in pregnancy was evaluated by comparing the predicted f u with experimentally observed f u values of 32 drugs taken from the literature. Results The pregnancy population PBPK model successfully predicted the pharmacokinetics of cefazolin, cefuroxime, and cefradine at all tested stages of pregnancy. All predicted plasma concentrations fell within a 2-fold error range and 85% of the predicted concentrations within a 1.25-fold error range. The f u in pregnancy could be adequately predicted using the proposed scaling approach, although a slight underestimation was evident in case of drugs bound to α 1 -acidic glycoprotein. Conclusion Pregnancy population PBPK models can provide a valuable tool to predict a priori the pharmacokinetics of predominantly renally cleared drugs in pregnant women. These models can ultimately support informed decision making regarding optimal dosing regimens in this vulnerable special population

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multi-scale by nature, project work and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim® and MoBi® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug-drug or drug-metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

The project Services@MediGRID consortium established a tool set of grid-based biomedical services since 2008. The services are related to genetic analysis, genome data visualization, and pharmacokinetic modeling. Furthermore, business concepts for these services have been examined which are supported by an accounting and billing service. While the tools cover a whole service chain for biomedicine, the business concepts are rather heterogeneous. However, the overall addressed target market areas show promising potential. In addition, a structured coaching process reduces friction in the technology transfer from grid computing to biomedicine. This should be considered for similar future endeavors. (C) 2011 Elsevier B.V. All rights reserved.

In clinical development stages, an a priori assessment of the sensitivity of the pharmacokinetic behavior with respect to physiological and anthropometric properties of human (sub-) populations is desirable. A physiology-based pharmacokinetic (PBPK) population model was developed that makes use of known distributions of physiological and anthropometric properties obtained from the literature for realistic populations. As input parameters, the simulation model requires race, gender, age, and two parameters out of body weight, height and body mass index. From this data, the parameters relevant for PBPK modeling such as organ volumes and blood flows are determined for each virtual individual. The resulting parameters were compared to those derived using a previously published model (P(3)M). Mean organ weights and blood flows were highly correlated between the two models, despite the different methods used to generate these parameters. The inter-individual variability differed greatly especially for organs with a log-normal weight distribution (such as fat and spleen). Two exemplary population pharmacokinetic simulations using ciprofloxacin and paclitaxel as model drugs showed good correlation to observed variability. A sensitivity analysis demonstrated that the physiological differences in the virtual individuals and intrinsic clearance variability were equally influential to the pharmacokinetic variability but were not additive. In conclusion, the new population model is well suited to assess the influence of individual physiological variability on the pharmacokinetics of drugs. It is expected that this new tool can be beneficially applied in the planning of clinical studies.