Rational Selection of Bio-Enabling Oral Drug Formulations - A PEARRL Commentary

Predictions of biorelevant solubility change during dispersion and digestion of lipid-based formulations

Computational approaches are increasingly explored in development of drug products, including the development of lipid-based formulations (LBFs), to assess their feasibility for achieving adequate oral absorption at an early stage. This study investigated the use of computational pharmaceutics approaches to predict solubility changes of poorly soluble drugs during dispersion and digestion in biorelevant media. Concentrations of 30 poorly water-soluble drugs were determined pre- and post-digestion with in-line UV probes using the MicroDISS Profiler™. Generally, cationic drugs displayed higher drug concentrations post-digestion, whereas for non-ionized drugs there was no discernible trend between drug concentration in dispersed and digested phase. In the case of anionic drugs there tended to be a decrease or no change in the drug concentration post-digestion. Partial least squares modelling was used to identify the molecular descriptors and drug properties which predict changes in solubility ratio in long-chain LBF pre-digestion (R2 of calibration = 0.80, Q2 of validation = 0.64) and post-digestion (R2 of calibration = 0.76, Q2 of validation = 0.72). Furthermore, multiple linear regression equations were developed to facilitate prediction of the solubility ratio pre- and post-digestion. Applying three molecular descriptors (melting point, LogD, and number of aromatic rings) these equations showed good predictivity (pre-digestion R2 = 0.70, and post-digestion R2 = 0.68). The model developed will support a computationally guided LBF strategy for emerging poorly water-soluble drugs by predicting biorelevant solubility changes during dispersion and digestion. This facilitates a more data-informed developability decision making and subsequently facilitates a more efficient use of formulation screening resources.

Oral absorption from surfactant-based drug formulations: the impact of molecularly dissolved drug on bioavailability

Enabling drug formulations are often required to ensure sufficient absorption after oral administration of poorly soluble drugs. While these formulations typically increase the apparent solubility of the drug, it is widely acknowledged that only molecularly dissolved, i.e. free fraction of the drug, is prone for direct absorption, while colloid-associated drug does not permeate to the same extent. In the present study, we aimed at comparing the effect of molecularly and apparently (i.e., the sum of molecularly and colloid-associated drug) dissolved drug concentrations on the oral absorption of a poorly water-soluble drug compound, Alectinib. Mixtures of Alectinib and respectively 50%, 25%, 12.5%, and 3% sodium lauryl sulfate (SLS) relative to the dose were prepared and small-scale dissolution tests were performed under simulated fed and fasted state conditions. Both the molecularly and apparently dissolved drug concentrations were assessed in parallel using microdialysis and centrifugation/filtration sampling, respectively. The data served as the basis for an in vitro-in vivo correlation (IVIVC) and as input for a GastroPlusTM physiologically based biopharmaceutics model (PBBM). It was shown that with increasing the content of SLS the apparently dissolved drug in FeSSIF and FaSSIF increased to a linear extent and thus, the predicted in vivo performance of the 50% SLS formulation, based on apparently dissolved drug, would outperform all other formulations. Against common expectation, however, the free (molecularly dissolved) drug concentrations were found to vary with SLS concentrations as well, yet to a minor extent. A systematic comparison of solubilized and free drug dissolution patterns at different SLS contents of the formulations and prandial states allowed for interesting insights into the complex dissolution- / supersaturation-, micellization-, and precipitation-behavior of the formulations. When comparing the in vitro datasets with human pharmacokinetic data from a bioequivalence study, it was shown that the use of molecularly dissolved drug resulted in an improved IVIVC. By incorporating the in vitro dissolution datasets into the GastroPlusTM PBBM, the apparently dissolved drug concentrations resulted in both, a remarkable overprediction of plasma concentrations as well as a misprediction of the influence of SLS on systemic exposure. In contrast, by using the molecularly dissolved drug (i.e., free fraction) as the model input, the predicted plasma concentration-time profiles were in excellent agreement with observed data for all formulations under both fed and fasted conditions. By combining an advanced in vitro assessment with PBBM, the present study confirmed that only the molecularly dissolved drug, and not the colloid-associated drug, is available for direct absorption.

Comparative Analysis of Chemical Descriptors by Machine Learning Reveals Atomistic Insights into Solute-Lipid Interactions

This study explores the research area of drug solubility in lipid excipients, an area persistently complex despite recent advancements in understanding and predicting solubility based on molecular structure. To this end, this research investigated novel descriptor sets, employing machine learning techniques to understand the determinants governing interactions between solutes and medium-chain triglycerides (MCTs). Quantitative structure-property relationships (QSPR) were constructed on an extended solubility data set comprising 182 experimental values of structurally diverse drug molecules, including both development and marketed drugs to extract meaningful property relationships. Four classes of molecular descriptors, ranging from traditional representations to complex geometrical descriptions, were assessed and compared in terms of their predictive accuracy and interpretability. These include two-dimensional (2D) and three-dimensional (3D) descriptors, Abraham solvation parameters, extended connectivity fingerprints (ECFPs), and the smooth overlap of atomic position (SOAP) descriptor. Through testing three distinct regularized regression algorithms alongside various preprocessing schemes, the SOAP descriptor enabled the construction of a superior performing model in terms of interpretability and accuracy. Its atom-centered characteristics allowed contributions to be estimated at the atomic level, thereby enabling the ranking of prevalent molecular motifs and their influence on drug solubility in MCTs. The performance on a separate test set demonstrated high predictive accuracy (RMSE = 0.50) for 2D and 3D, SOAP, and Abraham Solvation descriptors. The model trained on ECFP4 descriptors resulted in inferior predictive accuracy. Lastly, uncertainty estimations for each model were introduced to assess their applicability domains and provide information on where the models may extrapolate in chemical space and, thus, where more data may be necessary to refine a data-driven approach to predict solubility in MCTs. Overall, the presented approaches further enable computationally informed formulation development by introducing a novel in silico approach for rational drug development and prediction of dose loading in lipids.

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. Despite recent advances, there is limited modelling guidance specific to drug product development and a trend towards suboptimal modelling practices, resulting in models that may not give reliable predictions in practice. There is an overwhelming focus on benchtop experimental outcomes obtained for a specific modelling aim, leaving the capabilities of data scraping or the use of combined modelling approaches yet to be fully explored. Moreover, the preference for high accuracy can lead to a reliance on black box methods over interpretable models. This further limits the widespread adoption of machine learning as black boxes yield models that cannot be easily understood for the purposes of enhancing product performance. In this review, recommendations for conducting machine learning research for drug product development to ensure trustworthiness, transparency, and reliability of the models produced are presented. Finally, possible future directions on how research in this area might develop are discussed to aim for models that provide useful and robust guidance to formulators.

Leveraging the use of in vitro and computational methods to support the development of enabling oral drug products: An InPharma commentary

Due to the strong tendency towards poorly soluble drugs in modern development pipelines, enabling drug formulations such as amorphous solid dispersions, cyclodextrins, co-crystals and lipid-based formulations are frequently applied to solubilize or generate supersaturation in gastrointestinal fluids, thus enhancing oral drug absorption. Although many innovative in vitro and in silico tools have been introduced in recent years to aid development of enabling formulations, significant knowledge gaps still exist with respect to how best to implement them. As a result, the development strategy for enabling formulations varies considerably within the industry and many elements of empiricism remain. The InPharma network aims to advance a mechanistic, animal-free approach to the assessment of drug developability. This commentary focuses current status and next steps that will be taken in InPharma to identify and fully utilize 'best practice' in vitro and in silico tools for use in physiologically based biopharmaceutic models.

Physiological Buffer Effects in Drug Supersaturation - A Mechanistic Study of Hydroxypropyl Cellulose as Precipitation Inhibitor

Phosphate buffer is predominantly used instead of the more physiological bicarbonate buffer, as the latter requires a technical solution of adequate gas mixing. Recent pioneering work on how bicarbonate buffer affected drug supersaturation revealed interesting effects that call for more mechanistic understanding. Therefore, this study used hydroxypropyl cellulose as a model precipitation inhibitor and real-time desupersaturation testing was conducted with the drugs bifonazole, ezetimibe, tolfenamic acid and triclabendazole. Specific buffer effects for the different compounds were noted and overall, statistical significance was found for the precipitation induction time (p = 0.0088). Interestingly, molecular dynamics simulation revealed a conformational effect of the polymer in the presence of the different buffer types. Subsequent molecular docking trials suggested a stronger interaction energy of drug and polymer in the presence of phosphate compared to bicarbonate buffer (p =0.0010). In conclusion, a better mechanistic understanding of how different buffers affect drug-polymer interactions regarding drug supersaturation was achieved. Further mechanisms may account for the overall buffer effects and additional research on drug supersaturation is certainly needed, but it can already be concluded that bicarbonate buffering should be used more often for in vitro testing in drug development.

Application of biorelevant in vitro assays for the assessment and optimization of ASD-based formulations for pediatric patients

Amorphous solid dispersions (ASD) have been a successful formulation strategy to overcome the poor aqueous solubility of many novel drugs, but the development of pediatric formulations presents a special challenge due to variable gastrointestinal conditions in children. It was the aim of this work to design and apply a staged biopharmaceutical test protocol for the in vitro assessment of ASD-based pediatric formulations. Ritonavir was used as a model drug with poor aqueous solubility. Based on the commercial ASD powder formulation, a mini-tablet and a conventional tablet formulation were prepared. Drug release from the three formulations was studied in different biorelevant in vitro assays (i.e. MicroDiss, two-stage, transfer model, tiny-TIM) to consider different aspects of human GI physiology. Data from the two-stage and transfer model tests indicated that by controlled disintegration and dissolution excessive primary precipitation can be prevented. However, this advantage of the mini-tablet and tablet formulation did not translate into better performance in tiny-TIM. Here, the in vitro bioaccessibility was comparable for all three formulations. In the future, the staged biopharmaceutical action plan established herein will support the development of ASD-based pediatric formulations by improving the mechanistic understanding so that formulations are developed for which drug release is robust against variable physiological conditions.

Trends in oral small-molecule drug discovery and product development based on product launches before and after the Rule of Five

In 1997, the 'Rule of Five' (Ro5) suggested physicochemical limitations for orally administered drugs, based on the analysis of chemical libraries from the early 1990s. In this review, we report on the trends in oral drug product development by analyzing products launched between 1994 and 1997 and between 2013 and 2019. Our analysis confirmed that most new oral drugs are within the Ro5 descriptors; however, the number of new drug products of drugs with molecular weight (MW) and calculated partition coefficient (clogP) beyond the Ro5 has slightly increased. Analysis revealed that there is no single scientific or technological reason for this trend, but that it likely results from incremental advances are being made in molecular biology, target diversity, drug design, medicinal chemistry, predictive modeling, drug metabolism and pharmacokinetics, and drug delivery.

Developing Clinically Relevant Dissolution Specifications (CRDSs) for Oral Drug Products: Virtual Webinar Series

A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

Mucin-Protected Caco-2 Assay to Study Drug Permeation in the Presence of Complex Biorelevant Media

The poor solubility and permeability of compounds beyond Lipinski’s Rule of Five (bRo5) are major challenges for cell-based permeability assays. Due to their incompatibility with gastrointestinal components in biorelevant media, the exploration of important questions addressing food effects is limited. Thus, we established a robust mucin-protected Caco-2 assay to allow the assessment of drug permeation in complex biorelevant media. To do that, the assay conditions were first optimized with dependence of the concentration of porcine mucin added to the cells. Mucin-specific effects on drug permeability were evaluated by analyzing cell permeability values for 15 reference drugs (BCS class I–IV). Secondly, a sigmoidal relationship between mucin-dependent permeability and fraction absorbed in human (f_a) was established. A case study with venetoclax (BCS class IV) was performed to investigate the impact of medium complexity and the prandial state on drug permeation. Luminal fluids obtained from the tiny-TIM system showed a higher solubilization capacity for venetoclax, and a better read-out for the drug permeability, as compared to FaSSIF or FeSSIF media. In conclusion, the mucin-protected Caco-2 assay combined with biorelevant media improves the mechanistic understanding of drug permeation and addresses complex biopharmaceutical questions, such as food effects on oral drug absorption.

Bio-enabling strategies to mitigate the pharmaceutical food effect: a mini review

The concomitant administration of oral drugs with food can result in significant changes in bioavailability, leading to variable pharmacokinetics and considerable clinical implications, such as over- or under-dosing. Consequently, there is increasing demand for bio-enabling formulation strategies to reduce variability in exposure between the fasted and fed state and/or mitigate the pharmaceutical food effect. The current review critically evaluates technologies that have been implemented to overcome the positive food effects of pharmaceutical drugs, including, lipid-based formulations, nanosized drug preparations, cyclodextrins, amorphisation and solid dispersions, prodrugs and salts. Additionally, improved insight into preclinical models for predicting the food effect is provided. Despite the wealth of research, this review demonstrates that application of optimal formulation strategies to mitigate the positive food effects and the evaluation in preclinical models is not a universal approach, and improved standardisation of models to predict the food effects would be desirable. Ultimately, the successful reformulation of specific drugs to eliminate the food effect provides a panoply of advantages for patients with regard to clinical efficacy and compliance.

Hydroxypropyl Cellulose for Drug Precipitation Inhibition: From the Potential of Molecular Interactions to Performance Considering Microrheology

There has been recent interest in using hydroxypropyl cellulose (HPC) for supersaturating drug formulations. This study investigated the potential for molecular HPC interactions with the model drug celecoxib by integrating novel approaches in the field of drug supersaturation analysis. Following an initial polymer characterization study, quantum-chemical calculations and molecular dynamics simulations were complemented with results of inverse gas chromatography and broadband diffusing wave spectroscopy. HPC performance was studied regarding drug solubilization and kinetics of desupersaturation using different grades (i.e., HPC-UL, SSL, SL, and L). The results suggested that the potential contribution of dispersive interactions and hydrogen bonding depended strongly on the absence or presence of the aqueous phase. It was proposed that aggregation of HPC polymer chains provided a complex heterogeneity of molecular environments with more or less excluded water for drug interaction. In precipitation experiments at a low aqueous polymer concentration (i.e., 0.01%, w/w), grades L and SL appeared to sustain drug supersaturation better than SSL and UL. However, UL was particularly effective in drug solubilization at pH 6.8. Thus, a better understanding of drug-polymer interactions is important for formulation development, and polymer blends may be used to harness the combined advantages of individual polymer grades.

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. These aDS ratios and drug descriptors were used to train the ML models. When compared, the ANNs outperformed PLS for both sLBF_Capmul^MC (r² 0.90 vs. 0.56) and sLBF_Maisine^LC (r² 0.83 vs. 0.62), displaying smaller root mean square errors (RMSEs) and residuals upon training and testing. Across all the models, the descriptors involving reactivity and electron density were most important for prediction. This pilot study showed that ML can be employed to predict the propensity for supersaturation in LBFs, but even larger datasets need to be evaluated to draw final conclusions.