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Computer Aided Biopharmaceutical Characterization and Computer Simulation

Author(s): Jithin Raj, Arathy Jolly, Smitha K. Nair, Dinesh Kumar B, K. Krishnakumar
The biopharmaceutical evaluation of medications is essential at various stages of drug research and development. Pharmaceutical profiling can be used to locate a suitable ‘drug-like molecule for preclinical and clinical development in the early stages, and prolonged biopharmaceutical evaluation can be used to guide formulation strategy or forecast the influence of food on medication absorption in the later stages. The development and evaluation of in silica tools capable of identifying critical factors (i.e., drug physicochemical properties, dosage form factors) influencing drug in vivo performance, and predicting drug absorption based on the selected data set(s) of input factors has grown in response to a growing concern for biopharmaceutical characterization of drugs pharmaceutical products [1].
Different drug administration routes can be confirmed using an in silico pharmacokinetic (PK) model. Prediction of the pharmacokinetics of orally delivered medicines has been the main focus [2]. Drug absorption from the gastrointestinal (GI) tract is a complicated interaction of many elements (e.g., drug physicochemical qualities, physiological parameters, and formulation-related factors), and accurately modelling it in silico has been a major difficulty [3]. Starting with the pH-partition hypothesis and progressing to more complicated models like the Compartmental Absorption and Transit (CAT) model, many qualitative/quantitative techniques have been proposed. Yu et al. created an ideal overview of these models, categorising them as quasi-equilibrium, steady-state, and dynamic models [4].
In recent years, a lot of effort has gone into developing and promoting dynamic models of GI tract physiology in terms of drug transit, solubility, and absorption. The Advanced Dissolution, Absorption, and Metabolism (ADAM) model, the Grass model, the GI-Transit-Absorption (GITA) model, the CAT model, and the Advanced CAT (ACAT) model are all examples of these models. Gastro PlusTM, Sim CYP, PK-Sim ®, IDEATM (no longer available), Cloe ® PK, Cloe ® HIA, and INTELLIPHARM ® PKCR are just a few of the commercial software packages that have included them [5]. Dynamic models can predict both the fraction of dosage absorbed and the pace of drug absorption due to the dynamic interpretation of the processes a drug goes through in the GI tract and can be linked to PK models to examine plasma concentration-time profiles. The impact of food on drug absorption, as well as the potential impact of intestinal transporters and metabolism, can be investigated, leading to a better understanding of the observed pharmacokinetics and guiding further formulation attempts to mitigate these effects.
In comparison to in vivo investigations, in silico simulation techniques have a clear benefit in terms of resource and time investment. They can also be used to screen virtual compounds. As a result, the number of tests necessary for compound selection and development, as well as the associated costs and time, is significantly decreased. In addition, when traditional PK analysis is constrained, such as when intravenous data is absent due to poor drug solubility and/or if the drug has nonlinear kinetics, in silico approaches can be used to estimate oral drug absorption. Many research papers have examined and addressed the predictive properties of such mechanism-based models, emphasising both their benefits and potential limitations [6].
Selected research involving the use of GI simulation technology (GIST), namely Gastro PlusTM simulation technology, will be examined in the following sections. The basic principles of GIST will be discussed, as well as the advantages and disadvantages of using this mechanistic approach to predict oral drug absorption, estimate the influence of drug and/or formulation properties on the resulting absorption profile, predict the effects of food, assess the relationship between in vitro and in vivo data, and aid biowaiver justification [7].
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