Individualized Biochemical Profiling in Drug Design: Integrating MultiOmics, Nanotechnology, and Machine Learning.
TL;DR
How the amalgamation of these three fields (metabolomics, targeted nanotechnology, and machine learning) has the potential to reshape clinical interventions and allow researchers to refine drug reactions at the individual level is explored.
Abstract
The introduction of individualized biochemical profiles is allowing to revolutionize modern medicinal chemistry by providing more comprehensive data for the development of drugs, their refinement and clinical application. Conventional methods frequently underestimate inter-individual variability, resulting in inferior efficacy or adverse reactions. This new approach highlights the importance of personalized biochemical signatures in moulding pharmacokinetics and pharmacodynamics of drug candidates in accordance with the genomic variations, epigenetic alterations, and interaction of the host with its microbiome. The said parameters influence the absorption, distribution, metabolism, and excretion (ADME), forcing a re-evaluating the classical drug designing model. Individualized biochemical profiling will be fuelled by the combined impact of pharmacogenomics, high-throughput screening, and quantitative structure-activity relationship (QSAR) models, enabling the improvement of drug candidates better suited to an individual's metabolic and enzymatic ranges. This approach will notably help in predicting druginduced liver injury (DILI) and other organ-specific toxicities, allowing improved safety with novel treatments before clinical trials. In addition, individualized biochemical data can also enhance the accuracy of nanocarrier-based drug delivery systems by combining enzyme and receptor expression patterns, resulting in better tissue targeting and fewer off-target effects. This narrative review was conducted through a structured search of peer-reviewed literature from leading scientific databases, with emphasis on recent and translationally relevant studies. The article aims to explore how the amalgamation of these three fields (metabolomics, targeted nanotechnology, and machine learning) has the potential to reshape clinical interventions and allow researchers to refine drug reactions at the individual level.