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Hypothesis: Solubilization is a fundamental process that underpins various technologies in the pharmaceutical and chemical industry. However, knowledge of the location, orientation and interactions of solubilized molecules in the micelles is still limited. We expect all-atom molecular dynamics simulations to improve the molecular-level understanding of solubilization and to enable its in silico prediction., Methods: The solubilization of six drugs in intestinal mixed micelles composed of taurocholate and dioleoyl phosphatidylcholine was simulated by molecular dynamics in explicit water and measured experimentally by liquid chromatography. The location and orientation of the solubilized drugs were visualized by cumulative radial distribution functions and interactions were characterized by radial distribution function ratios and hydrogen bonding., Findings: A new simulation-derived parameter was defined, which accounts for drug-micelle and drug-water interactions and correlates (R 2  = 0.83) with the experimentally measured solubilization. Lipophilicity was found to govern the location of all drugs in the micelle (hydrophobic core, palisade layer or on the surface), while hydrogen bonding was crucial for orientation and solubilization of two of the molecules. The study demonstrates that explicit, hydrogen bond-forming water molecules are vital for accurate prediction of solubilization and provides a comprehensive framework for quantitative studies of drug location and orientation within the micelles., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 The Author(s). Published by Elsevier Inc. All rights reserved.)

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log  D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log  S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

The aim of the current study was (1) to develop an automation-based protocol for in vitro assessment of enzymatic drug stability at fasted- and fed-state intestinal conditions, (2) to characterize the inter-individual variability of drug degradation in fasted- and fed-state human intestinal fluids, and (3) to compare the obtained in vitro results to drug degradation in human intestinal fluids by taking variability into account. In human intestinal fluids, drug degradation displayed large inter-individual variability, with coefficients of variance generally ranging between 30 and 70 %. The effect of food on the inter-individual variability was highly dependent on the type of drug. The increase of pH in the range between 5.0 and 7.0 significantly accelerated the degradation rate of the studied drugs both in the in vitro and ex vivo experiments. In contrast, the increase of bile salt and phospholipid concentrations in the in vitro screen decreased strongly the degradation rate of the hydrophobic drugs. The developed automated in vitro screen mimicked relatively well the ex vivo degradation of all drugs in the fasted state, whereas in the fed state the degradation of only one of the drugs was adequately reproduced., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Zahari Vinarov reports financial support was provided by Bulgarian National Science Fund. Patrick Augustijns reports financial support was provided by Flanders Innovation & Entrepreneurship., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Current regulatory guidelines on drug-food interactions recommend an early assessment of food effect to inform clinical dosing instructions, as well as a pivotal food effect study on the to-be-marketed formulation if different from that used in earlier trials. Study waivers are currently only granted for BCS class 1 drugs. Thus, repeated food effect studies are prevalent in clinical development, with the initial evaluation conducted as early as the first-in-human studies. Information on repeated food effect studies is not common in the public domain. The goal of the work presented in this manuscript from the Food Effect PBPK IQ Working Group was to compile a dataset on these studies across pharmaceutical companies and provide recommendations on their conduct. Based on 54 studies collected, we report that most of the repeat food effect studies do not result in meaningful differences in the assessment of the food effect. Seldom changes observed were more than twofold. There was no clear relationship between the change in food effect and the formulation change, indicating that in most cases, once a compound is formulated appropriately within a specific formulation technology, the food effect is primarily driven by inherent compound properties. Representative examples of PBPK models demonstrate that following appropriate validation of the model with the initial food effect study, the models can be applied to future formulations. We recommend that repeat food effect studies should be approached on a case-by-case basis taking into account the totality of the evidence including the use of PBPK modeling., (© 2023. The Author(s), under exclusive licence to American Association of Pharmaceutical Scientists.)

For oral drug products, in vitro dissolution is the most used surrogate of in vivo dissolution and absorption. In the context of drug product quality, safe space is defined as the boundaries of in vitro dissolution, and relevant quality attributes, within which drug product variants are expected to be bioequivalent to each other. It would be highly desirable if the safe space could be established via a direct link between available in vitro data and in vivo pharmacokinetics. In response to the challenges with establishing in vitro-in vivo correlations (IVIVC) with traditional modeling approaches, physiologically based biopharmaceutics modeling (PBBM) has been gaining increased attention. In this manuscript we report five case studies on using PBBM to establish a safe space for BCS Class 2 and 4 across different companies, including applications in an industrial setting for both internal decision making or regulatory applications. The case studies provide an opportunity to reflect on practical vs. ideal datasets for safe space development, the methodologies for incorporating dissolution data in the model and the criteria used for model validation and application. PBBM and safe space, still represent an evolving field and more examples are needed to drive development of best practices., (Copyright © 2021 American Pharmacists Association. Published by Elsevier Inc. All rights reserved.)

Pharmacokinetic (PK) profiles of a range of bedaquiline (BDQ) long-acting injectable (LAI) microsuspensions in rats after parenteral ( i.e. , intramuscular and subcutaneous) administration were correlated with the in vitro intrinsic dissolution rate (IDR) and thermodynamic solubility of BDQ in media varying in surfactant type and concentration to better understand the impact of different nonionic surfactants on the in vivo performance of BDQ LAI microsuspensions. All LAI formulations had a similar particle size distribution. The investigated surfactants were d-α-tocopheryl polyethylene glycol 1000 succinate (TPGS), poloxamer 338, and poloxamer 188. Furthermore, the relevance of medium complexity by using a biorelevant setup to perform in vitro measurements was assessed by comparing IDR and thermodynamic solubility results obtained in biorelevant media and formulation vehicle containing different surfactants in varying concentrations. In the presence of a surfactant, both media could be applied to obtain in vivo representative dissolution and solubility data because the difference between the biorelevant medium and formulation vehicle was predominantly nonsignificant. Therefore, a more simplistic medium in the presence of a surfactant was preferred to obtain in vitro measurements to predict the in vivo PK performance of LAI aqueous suspensions. The type of surfactant influenced the PK profiles of BDQ microsuspensions in rats, which could be the result of a surfactant effect on the IDR and/or thermodynamic solubility of BDQ. Overall, two surfactant groups could be differentiated: TPGS and poloxamers. Most differences between the PK profiles ( i.e. , maximum concentration observed, time of maximum concentration observed, and area under the curve) were observed during the first 21 days postdose, the time period during which particles in the aqueous suspension are expected to dissolve.

This report summarizes the proceedings for Day 3 of the workshop titled "Current State and Future Expectations of Translational Modeling Strategies toSupportDrug Product Development, Manufacturing Changes and Controls". From a drug product quality perspective, patient-centric product development necessitates the development of clinically relevant drug product specifications (CRDPS). In this regard, Physiologically Based Biopharmaceutics modeling (PBBM) is a viable tool to establish links between in-vitro to in-vivo data, and support with establishing CRDPS. The theme of day 3 was practical applications of PBBM to support drug product quality. In this manuscript, case studies from US FDA, EMA and pharmaceutical industry on applications of PBBM in drug product quality are summarized which include 1) regulatory agency's perspectives on establishing the safe space and achieving study waivers, 2) model-informed risk assessment on the effects of acid reducing agents, bridging of dissolution methods, food effect, and formulation selection, and 3) understanding clinical formulation performance. Breakout session discussions focused on four topics - 1) terminologies related to physiologically based modeling in support of drug product quality, 2) regulatory harmonization on evidentiary standards, 3) CRDPS approaches and 4) bridging between biorelevant and quality control (QC) dissolution methods., (Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

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 GastroPlus™ (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 (AUC 0-tlast ), Maximal concentration (C max ), half-life (t 1/2 )) 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 AUC 0-tlast and around 90% of the simulations were within 10-fold error for AUC 0-tlast . Oral bioavailability (F oral ) 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 AUC 0-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, AUC 0-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., (Copyright © 2020 Elsevier B.V. All rights reserved.)

In vitro dissolution experiments are used to qualitatively assess the impact of formulation composition and process changes on the drug dosage form performance. However, the use of dissolution data to quantitatively predict changes in the absorption profile remains limited. Physiologically-based Pharmacokinetic(s) (PBPK) models facilitate incorporation of in vitro dissolution experiments into mechanistic oral absorption models to predict in vivo oral formulation performance, and verify if the drug product dissolution method is biopredictive or clinically relevant. Nevertheless, a standardized approach for using dissolution data within PBPK models does not yet exist and the introduction of dissolution data in PBPK relies on a case by case approach which accommodates from differences in release mechanism and limitations to drug absorption. As part of the Innovative Medicines Initiative (IMI) Oral Biopharmaceutics Tools (OrBiTo) project a cross-work package was set up to gather a realistic understanding of various approaches used and their areas of applications. This paper presents the approaches shared by academic and industrial scientists through the OrBiTo project to integrate dissolution data within PBPK software to improve the prediction accuracy of oral formulations in vivo. Some general recommendations regarding current use and future improvements are also provided., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The effect of food on pharmacokinetic properties of drugs is a commonly observed occurrence affecting about 40% of orally administered drugs. Within the pharmaceutical industry, significant resources are invested to predict and characterize a clinically relevant food effect. Here, the predictive performance of physiologically based pharmacokinetic (PBPK) food effect models was assessed via de novo mechanistic absorption models for 30 compounds using controlled, pre-defined in vitro, and modeling methodology. Compounds for which absorption was known to be limited by intestinal transporters were excluded in this analysis. A decision tree for model verification and optimization was followed, leading to high, moderate, or low food effect prediction confidence. High (within 0.8- to 1.25-fold) to moderate confidence (within 0.5- to 2-fold) was achieved for most of the compounds (15 and 8, respectively). While for 7 compounds, prediction confidence was found to be low (> 2-fold). There was no clear difference in prediction success for positive or negative food effects and no clear relationship to the BCS category of tested drug molecules. However, an association could be demonstrated when the food effect was mainly related to changes in the gastrointestinal luminal fluids or physiology, including fluid volume, motility, pH, micellar entrapment, and bile salts. Considering these findings, it is recommended that appropriately verified mechanistic PBPK modeling can be leveraged with high to moderate confidence as a key approach to predicting potential food effect, especially related to mechanisms highlighted here.

Acid-reducing agents (ARAs) such as antacids, histamine-2 receptor antagonists, and proton pump inhibitors are widely prescribed in several disease states. In the case of a basic drug with pH-dependent solubility, concomitant administration with an ARA may reduce drug absorption and systemic exposure, potentially resulting in the loss of efficacy. Therefore, it is important to assess a drug's susceptibility to pH-dependent drug-drug interactions (DDIs) during drug development, to characterize the DDI with clinical studies (as needed), and include appropriate instructions in the label. Given the ability of physiologically based biopharmaceutics modeling (PBBM) to directly link pharmacokinetics with physiological parameters, compound and formulation properties, these models are well positioned to address the DDI effects of ARAs. In this article, we describe application of PBBM for biopharmaceutics risk assessment, and to guide formulation and clinical development strategies. Seven case studies from 5 pharmaceutical companies are presented demonstrating cross-industry experience in PBBM prediction of pH-dependent DDIs. These case studies are for BCS 2 and 4 compounds, with adequate clinical data to assess the accuracy of the predictions. Based on these examples, and previously published literature, we propose a pragmatic PBBM workflow to inform clinical development and regulatory decisions in ARA risk assessment., (Copyright © 2020 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

The establishment of an in vitro-in vivo correlation (IVIVC) is considered the gold standard to establish in vivo relevance of a dissolution method and to utilize dissolution data in the context of regulatory bioequivalence questions, including the development of dissolution specifications. However, several recent publications, including industry surveys and reviews from regulatory agencies, have indicated a low success rate for IVIVCs, especially for immediate-release formulations. In recent years, the use of physiologically based pharmacokinetics (PBPK) and absorption modeling, as a tool to facilitate formulation development, has been attracting increased attention. This manuscript provides an industry perspective on the current challenges with establishing IVIVCs and the potential PBPK and absorption modeling offer to increase their impact. Case studies across both immediate-release and extended-release formulations from five pharmaceutical companies are utilized to demonstrate how physiologically based IVIVC (PB-IVIVC) may facilitate drug product understanding and to inform bioequivalence assessment and clinically relevant specifications. Finally, PB-IVIVC best practices and a strategy for model development and application are proposed.

Food can alter the absorption of orally administered drugs. Biopharmaceutics physiologically based pharmacokinetic (PBPK) modeling offers the possibility to simulate a compound's pharmacokinetics under fasted or fed states. To advance the utility of PBPK modeling, with a view to regulatory impact, we have pooled our experience across 4 pharmaceutical companies to propose a general multistep PBPK workflow leveraging pre-existing clinical data for immediate-release formulations of Biopharmaceutics Classification System I and II compounds. With this strategy, we wish to promote pragmatic PBPK approaches for compounds where absorption is well understood, that is, compounds with moderate-to-high permeability that are not substrates for uptake transporters. Five case studies demonstrate how food effect can be well predicted using appropriately established and validated models. The case studies integrate solubility and dissolution data for initial model development and apply a "middle-out" validation with clinical data in one prandial state. Then, whenever possible, a validation against both fasted and fed state data is recommended before application of the models prospectively for to-be-marketed formulations. Thus, when combined with limited clinical data, PBPK models could be used to simulate outcomes for new doses, formulations, or active pharmaceutical ingredient forms, in lieu of a clinical food-effect study., (Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Predicting oral bioavailability (F oral ) is of importance for estimating systemic exposure of orally administered drugs. Physiologically-based pharmacokinetic (PBPK) modelling and simulation have been applied extensively in biopharmaceutics recently. The Oral Biopharmaceutical Tools (OrBiTo) project (Innovative Medicines Initiative) aims to develop and improve upon biopharmaceutical tools, including PBPK absorption models. A large-scale evaluation of PBPK models may be considered the first step. Here we characterise the OrBiTo active pharmaceutical ingredient (API) database for use in a large-scale simulation study. The OrBiTo database comprised 83 APIs and 1475 study arms. The database displayed a median logP of 3.60 (2.40-4.58), human blood-to-plasma ratio of 0.62 (0.57-0.71), and fraction unbound in plasma of 0.05 (0.01-0.17). The database mainly consisted of basic compounds (48.19%) and Biopharmaceutics Classification System class II compounds (55.81%). Median human intravenous clearance was 16.9L/h (interquartile range: 11.6-43.6L/h; n=23), volume of distribution was 80.8L (54.5-239L; n=23). The majority of oral formulations were immediate release (IR: 87.6%). Human F oral displayed a median of 0.415 (0.203-0.724; n=22) for IR formulations. The OrBiTo database was found to be largely representative of previously published datasets. 43 of the APIs were found to satisfy the minimum inclusion criteria for the simulation exercise, and many of these have significant gaps of other key parameters, which could potentially impact the interpretability of the simulation outcome. However, the OrBiTo simulation exercise represents a unique opportunity to perform a large-scale evaluation of the PBPK approach to predicting oral biopharmaceutics., (Copyright © 2016 Elsevier B.V. All rights reserved.)

A novel alignment procedure for chromatographic signals with photodiode array detection is presented. At first, the complexity of the chromatographic signals is reduced by chemometric resolution of the pure constituents. For this, the application of multivariate curve resolution-alternating least squares leads to the decomposition of the multiway data block into a chemically meaningful bilinear model representing the chromatographic profiles and their spectral signatures. The flexible implementation of a spectral selectivity constraint allows the background to be differentiated from the constituent spectra. Hereby, the pure concentration profiles are obtained which are consequently individually aligned by correlation optimized warping. In its final step, the procedure reconstitutes the original data with the aligned chromatographic profiles and their corresponding spectra. The alignment is evaluated for two sets of chromatographic signals. The new procedure improves the original application of correlation optimized warping minimizing the risks of aligning noncorresponding chromatographic information.

This work presents near-infrared spectroscopy (NIRS) as an in-line process analyzer for monitoring protein unfolding and protein-lyoprotectant hydrogen bond interactions during freeze-drying. By implementing a noncontact NIR probe in the freeze-drying chamber, spectra of formulations containing a model protein immunoglobulin G (IgG) were collected each process minute. When sublimation was completed in the cake region illuminated by the NIR probe, the frequency of the amide A/II band (near 4850 cm(-1)) was monitored as a function of water elimination. These two features were well correlated during protein dehydration in the absence of protein unfolding (desired process course), whereas consistent deviations from this trend to higher amide A/II frequencies were shown to be related to protein unfolding. In formulations with increased sucrose concentrations, the markedly decreased amide A/II frequencies seen immediately after sublimation indicated an increased extent of hydrogen bond interaction between the protein's backbone and surrounding molecules. At the end of drying, there was evidence of nearly complete water substitution for formulations with 1%, 5%, and 10% sucrose. The presented approach shows promising perspectives for early fault detection of protein unfolding and for obtaining mechanistic process information on actions of lyoprotectants.

Extracts of Citri reticulatae pericarpium (PCR) are commonly used in the Traditional Chinese Medicine. The quality control of PCR is currently performed by single marker analysis, which can hardly describe the complexity of such natural samples. In this study, a fingerprint methodology for PCR based on high-performance liquid chromatography (HPLC) was developed and validated. A total of 69 fingerprints of authenticated PCR samples, commercial PCR samples, mixed peel samples, and other Citrus peels were recorded. Exploratory data analysis allowed optimizing the extraction procedure and detecting mixed peel samples. Once the optimizations were performed and the method validated, discrimination between the authentic PCR samples and all other samples was performed by p-Discriminant Partial Least Squares. The established model was able to differentiate between classes with a high reliability for each sample. Furthermore, evaluation of the score and loading plots of the model indicated nobiletin, tangeretin, naringin and hesperidin as important markers for the quality control of PCR., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Several Mallotus species (Euphorbiaceae) are used in Vietnam as edible plants or as traditional medicines for different indications, some related to the treatment of inflammatory diseases. This study investigated the antioxidant activities of 33 samples from 17 Vietnamese Mallotus species. We also evaluated potential cytotoxic activity against human cervix carcinoma HeLa and human lung fibroblast WI-38 cells. Our aim is to develop safe dietary supplements with a protective effect against various diseases caused by tissue damage and the acceleration of the aging process linked to reactive oxygen species. These tests allowed the identification of non-cytotoxic plant species exhibiting significant antiradical properties. These antioxidant properties may be explained by their polyphenol composition. The antioxidant activity of the most active Mallotus species was further analyzed with and without tannins removal. We also identified by LC-ESI-MS some flavonoids responsible for a part of this activity.

As herbal medicines have an important position in health care systems worldwide, their current assessment and quality control are a major bottleneck. Over the past decade, major steps were taken not only to improve the quality of the herbal products but also to develop analytical methods ensuring their quality. Nowadays, chromatographic fingerprinting is the generally accepted technique for the assessment and quality control of herbal products. This paper briefly considers the evolution of the regulations and guidelines on the quality control of herbal medicines, and reviews the established analytical techniques for herbal fingerprinting with an emphasis on the most recent developments, such as miniaturized techniques, new stationary phases, analysis at high temperatures and multi-dimensional chromatography. Accessory to the new analytical techniques, the chemometric data handling techniques applied are discussed. Chemometrics provide scientists with useful tools in understanding the huge amounts of data generated by the analytical advances and prove to be valuable for quality control, classification and modelling of, and discrimination between herbal fingerprints., (Copyright © 2011 Elsevier B.V. All rights reserved.)