Establishing a Pharmacoinformatics Repository of Approved Medicines: A Database to Support Drug Product Development.

Advanced predictive modeling approaches have harnessed data to fuel important innovations at all stages of drug development. However, the need for a machine-readable drug product library which consolidates many aspects of formulation design and performance remains largely unmet. This study presents a scripted, reproducible approach to database curation and explores its potential to streamline oral medicine development.

Impact of a short-term pharmacy study abroad Program: student outcomes and program evaluation.

Objective: This study examined the impact of a short-term study abroad program, focusing on program evaluation, attendee satisfaction, and acquired knowledge and skills. A questionnaire survey was conducted covering various aspects including demographics, program evaluation, and feedback.

Results: Results indicated higher female participation due to gender imbalances in pharmacy students in Egypt, with senior students recognizing the value of international experience.

Exploring career choices of pharmacy graduates over 15 years: A cross-sectional evaluation.

Introduction: Career opportunities for pharmacists beyond those commonly associated with the degree continue to emerge. A paucity of literature regarding evaluation of pharmacy graduate career paths over extended periods is apparent. Considering international pharmacy workforce capacity pressures, the primary study aim was to evaluate trends in career paths of pharmacy graduates.

Advancing algorithmic drug product development: Recommendations for machine learning approaches in drug formulation.

Artificial intelligence is a rapidly expanding area of research, with the disruptive potential to transform traditional approaches in the pharmaceutical industry, from drug discovery and development to clinical practice. Machine learning, a subfield of artificial intelligence, has fundamentally transformed in silico modelling and has the capacity to streamline clinical translation. This paper reviews data-driven modelling methodologies with a focus on drug formulation development.

Artificial Neural Networks to Predict the Apparent Degree of Supersaturation in Supersaturated Lipid-Based Formulations: A Pilot Study.

In response to the increasing application of machine learning (ML) across many facets of pharmaceutical development, this pilot study investigated if ML, using artificial neural networks (ANNs), could predict the apparent degree of supersaturation (aDS) from two supersaturated LBFs (sLBFs). Accuracy was compared to partial least squares (PLS) regression models. Equilibrium solubility in Capmul MCM and Maisine CC was obtained for 21 poorly water-soluble drugs at ambient temperature and 60 °C to calculate the aDS ratio.

Machine learning methods for prediction of food effects on bioavailability: A comparison of support vector machines and artificial neural networks.

Despite countless advances in recent decades across various in vitro, in vivo and in silico tools, anticipation of whether a drug will show a human food effect (FE) remains challenging. One means to predict potential FE involves probing any dependence between FE and drug properties. Accordingly, this study explored the potential for two machine learning (ML) algorithms to predict likely FE.

, , and Evaluation of Precipitation Inhibitors in Supersaturated Lipid-Based Formulations of Venetoclax.

The concept of using precipitation inhibitors (PIs) to sustain supersaturation is well established for amorphous formulations but less in the case of lipid-based formulations (LBF). This study applied a systematic -- approach to assess the merits of incorporating PIs in supersaturated LBFs (sLBF) using the model drug venetoclax. sLBFs containing hydroxypropyl methylcellulose (HPMC), hydroxypropyl methylcellulose acetate succinate (HPMCAS), polyvinylpyrrolidone (PVP), PVP--vinyl acetate (PVP/VA), Pluronic F108, and Eudragit EPO were assessed calculating a drug-excipient mixing enthalpy, using a PI solvent shift test, and finally, bioavailability was assessed in landrace pigs.

Exploring porcine gastric and intestinal fluids using microscopic and solubility estimates: Impact of placebo self-emulsifying drug delivery system administration to inform bio-predictive in vitro tools.

Validation and characterisation of in vitro and pre-clinical animal models to support bio-enabling formulation development is of paramount importance. In this work, post-mortem gastric and small intestinal fluids were collected in the fasted, fed state and at five sample-points post administration of a placebo Self-Emulsifying Drug Delivery System (SEDDS) in the fasted state to pigs. Cryo-TEM and Negative Stain-TEM were used for ultrastructure characterisation.

Applying Computational Predictions of Biorelevant Solubility Ratio Upon Self-Emulsifying Lipid-Based Formulations Dispersion to Predict Dose Number.

Computational approaches are increasingly utilised in development of bio-enabling formulations, including self-emulsifying drug delivery systems (SEDDS), facilitating early indicators of success. This study investigated if in silico predictions of drug solubility gain i.e.

Characterization of gastrointestinal transit and luminal conditions in pigs using a telemetric motility capsule.

Within preclinical research, the pig has become an important model in regulatory toxicology and pharmacokinetics, to assess oral dosage forms and to compare different formulation strategies. In addition, there are emerging application of the pig model to asses clinical dosing conditions in the fasted and fed state. In this study, the gastrointestinal transit conditions in male landrace pigs were studied with a telemetric motility capsule under fasted and postprandial conditions.

A Retrospective Biopharmaceutical Analysis of >800 Approved Oral Drug Products: Are Drug Properties of Solid Dispersions and Lipid-Based Formulations Distinctive?

Increasing numbers of poorly water soluble drugs in development has intensified need for bio-enabling formulations including Lipid-Based Formulations (LBF) and Solid Dispersions (SD). Resultantly, a data-driven approach is required to increase formulation development efficiency. This review provides a retrospective analysis of molecular and biopharmaceutical properties of drugs commercialised as LBFs or SDs.

Novel Biphasic Lipolysis Method To Predict Performance of Lipid-Based Formulations.

The absence of an intestinal absorption sink is a significant weakness of standard lipolysis methods, potentially leading to poor prediction of performance and an overestimation of drug precipitation. In addition, the majority of the described lipolysis methods only attempt to simulate intestinal conditions, thus overlooking any supersaturation or precipitation of ionizable drugs as they transition from the acidic gastric environment to the more neutral conditions of the intestine. The aim of this study was to develop a novel lipolysis method incorporating a two-stage gastric-to-intestinal transition and an absorptive compartment to reliably predict performance of lipid-based formulations (LBFs).