6533b82dfe1ef96bd12914f6

RESEARCH PRODUCT

IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with improved data and modelling strategies

Alex MattinsonDavid LindleyHelena EngmanManthena V.s. VarmaAnh-thu Nguyen-trungBinfeng XiaXavier PepinLinette RustonWei ZhuBill Van OsdolCeline OllierDavid GoodJonathan BrownLaurent BouluTalia FlanaganOlivier NicolasStaffan BergShanoo BudhdeoAarti PatelJan BevernageHandan HeSweta ModiRichard BarkerClaire JacksonChrister TannergrenXiojun RenStephane BeillesJ. Matthew WoodJohanna LaruPierre DaublainSara CarlertLouis HenrionJean-flaubert NguefackGunilla HanischEva KarlssonWen LinShruthi VaidhyanathanRobert CarrFan WuJin ZhangAmais AhmadJames M. MullinSari PappinenAndrea MoirKe SzetoChristine XuClaire PattersonDavid B. TurnerGuillaume LouitYuya WangTycho HeimbachRichard LloydFrans FranekMasoud JameiChristophe TistaertBertil AbrahamssonKerstin Julia SchäferLeon AaronsShriram M. PathakAdam S. DarwichSuet WongDónal MurphyAmin Rostami-hodjeganTimo KorjamoHelena ThörnKartrin SchmidMichael B. BolgerJohanna TuunainenMai Anh Nguyen

subject

Data AnalysisPhysiologically based pharmacokinetic modellingDatabases FactualAdministration OralPharmaceutical Science02 engineering and technologyMachine learningcomputer.software_genreModels Biological030226 pharmacology & pharmacyBiopharmaceuticsPharmaceutical Sciences03 medical and health sciences0302 clinical medicineSoftwarePharmacokineticsHumansClinical Trials as Topicbusiness.industryCompound specificBiopharmaceuticsGeneral MedicineFarmaceutiska vetenskaper021001 nanoscience & nanotechnologyBioavailabilityIntestinal AbsorptionPharmaceutical PreparationsDrug developmentPerformance indicatorArtificial intelligence0210 nano-technologybusinesscomputerSoftwareForecastingBiotechnology

description

Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption is much needed. Current status of predictive performance, within the confinement of commonly available in vitro data on drugs and formulations alongside systems information, were tested using 3 PBPK software packages (GI-Sim (ver.4.1), Simcyp® Simulator (ver.15.0.86.0), and GastroPlusTM (ver.9.0.00xx)). This was part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project. Fifty eight active pharmaceutical ingredients (APIs) were qualified from the OrBiTo database to be part of the investigation based on a priori set criteria on availability of minimum necessary information to allow modelling exercise. The set entailed over 200 human clinical studies with over 700 study arms. These were simulated using input parameters which had been harmonised by a panel of experts across different software packages prior to conduct of any simulation. Overall prediction performance and software packages comparison were evaluated based on performance indicators (Fold error (FE), Average fold error (AFE) and absolute average fold error (AAFE)) of pharmacokinetic (PK) parameters. On average, PK parameters (Area Under the Concentration-time curve (AUC0-tlast), Maximal concentration (Cmax), half-life (t1/2)) were predicted with AFE values between 1.11 and 1.97. Variability in FEs of these PK parameters was relatively high with AAFE values ranging from 2.08 to 2.74. Around half of the simulations were within the 2-fold error for AUC0-tlast and around 90% of the simulations were within 10-fold error for AUC0-tlast. Oral bioavailability (Foral) predictions, which were limited to 19 APIs having intravenous (i.v.) human data, showed AFE and AAFE of values 1.37 and 1.75 respectively. Across different APIs, AFE of AUC0-tlast predictions were between 0.22 and 22.76 with 70% of the APIs showing an AFE > 1. When compared across different formulations and routes of administration, AUC0-tlast for oral controlled release and i.v. administration were better predicted than that for oral immediate release formulations. Average predictive performance did not clearly differ between software packages but some APIs showed a high level of variability in predictive performance across different software packages. This variability could be related to several factors such as compound specific properties, the quality and availability of information, and errors in scaling from in vitro and preclinical in vivo data to human in vivo behaviour which will be explored further. Results were compared with previous similar exercise when the input data selection was carried by the modeller rather than a panel of experts on each in vitro test. Overall, average predictive performance was increased as reflected in smaller AAFE value of 2.8 as compared to AAFE value of 3.8 in case of previous exercise. QC 20200930

https://doi.org/10.1016/j.ejpb.2020.08.006