Kuentz, Martin
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In vitro methods to assess drug precipitation in the fasted small intestine – a PEARRL review
Objectives Drug precipitation in vivo poses a significant challenge for the pharmaceutical industry. During the drug development process, the impact of drug supersaturation or precipitation on the in vivo behaviour of drug products is evaluated with in vitro techniques. This review focuses on the small and full scale in vitro methods to assess drug precipitation in the fasted small intestine. Key findings Many methods have been developed in an attempt to evaluate drug precipitation in the fasted state, with varying degrees of complexity and scale. In early stages of drug development, when drug quantities are typically limited, small‐scale tests facilitate an early evaluation of the potential precipitation risk in vivo and allow rapid screening of prototype formulations. At later stages of formulation development, full‐scale methods are necessary to predict the behaviour of formulations at clinically relevant doses. Multicompartment models allow the evaluation of drug precipitation after transfer from stomach to the upper small intestine. Optimisation of available biopharmaceutics tools for evaluating precipitation in the fasted small intestine is crucial for accelerating the development of novel breakthrough medicines and reducing the development costs. Summary Despite the progress from compendial quality control dissolution methods, further work is required to validate the usefulness of proposed setups and to increase their biorelevance, particularly in simulating the absorption of drug along the intestinal lumen. Coupling results from in vitro testing with physiologically based pharmacokinetic modelling holds significant promise and requires further evaluation.
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. © 2023
Novel Biphasic Lipolysis Method To Predict in Vivo Performance of Lipid-Based Formulations
The absence of an intestinal absorption sink is a significant weakness of standard in vitro lipolysis methods, potentially leading to poor prediction of in vivo 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 in vivo performance of lipid-based formulations (LBFs). Drug absorption was mimicked by in situ quantification of drug partitioning into a decanol layer. The method was used to characterize LBFs from four studies described in the literature, involving three model drugs (i.e., nilotinib, fenofibrate, and danazol) where in vivo bioavailability data have previously been reported. The results from the novel biphasic lipolysis method were compared to those of the standard pH-stat method in terms of reliability for predicting the in vivo performance. For three of the studies, the novel biphasic lipolysis method more reliably predicted the in vivo bioavailability compared to the standard pH-stat method. In contrast, the standard pH-stat method was found to produce more predictive results for one study involving a series of LBFs composed of the soybean oil, glyceryl monolinoleate (Maisine CC), Kolliphor EL, and ethanol. This result was surprising and could reflect that increasing concentrations of ethanol (as a cosolvent) in the formulations may have resulted in greater partitioning of the drug into the decanol absorptive compartment. In addition to the improved predictivity for most of the investigated systems, this biphasic lipolysis method also uses in situ analysis and avoids time- and resource-intensive sample analysis steps, thereby facilitating a higher throughput capacity and biorelevant approach for characterization of LBFs.