Over the past decade, the pharmaceutical sector has seen a drastic increase in digitalization. However, this digitalization comes with unique challenges. For example, acquiring, scrutinizing, and applying knowledge from pharmacokinetic studies to solve clinical issues is complex and challenging. This challenge has encouraged the use of AI in assessing pharmacology studies such as PK samples in clinical trials. AI can handle large volumes of data from any pharmacokinetic study and ELISA assay development initiatives, and therefore pharmacokinetics labs are increasingly employing AI in experimental setups.
AI is a technology involving numerous tools and networks to mimic human intelligence. It utilizes software and systems to interpret and evaluate input data to perform independent tasks and objectives. Hence, its applications are used widely in the pharmaceutical sector. The current article highlights the applications of artificial intelligence and machine learning technologies to overcome data management and analysis challenges in pharmacokinetics laboratories.
Overcoming Data Management and Analysis Obstacles of PK Samples in Clinical Trials
AI has numerous applications in the development of a drug product. These applications include assisting in decision-making, personalized medicine, and managing clinical data and its use in future studies. One such example of AI in the pharmaceutical sector is E-VAI, developed by Eularis. This AI platform uses machine learning algorithms to generate analytical roadmaps based on key stakeholders, market share, and competitors to predict critical sales and marketing drivers. Such a robust platform can help marketing executives allocate resources to appropriate channels.
The drug development space has thousands and thousands of candidate drug molecules. However, assessing these compounds needs advanced technologies, making them expensive and time-consuming. AI and machine learning can address these challenges. AI can identify lead compounds, quickly validate the drug target, and optimize the drug structure design. Moreover, quantitative structure-activity relationship modeling tools can identify potential drug compounds. Today these tools have evolved into AI-based quantitative structure-activity relationship approaches, such as support vector machines, linear discriminant analysis, and random forest and decision trees. These combinations can speed up the analysis of quantitative structure-activity relationships.
Drug discovery and development can take over a decade, with an average cost of $2.8 billion. Moreover, nine out of ten drug compounds do not pass Phase II clinical trials and regulatory approvals. Here AI and machine learning can prove beneficial. Algorithms like extreme learning machines, deep neural networks, nearest-neighbor classifiers, SVMS, and RF can provide relative in vivo toxicity and activity data. Several pharmaceutical companies tie with IT companies to develop platforms to discover novel drug compounds.
Physicochemical properties such as degree of ionization, solubility, intrinsic drug permeability, and partition coefficient affect the pharmacokinetic properties of a drug and its target receptors. Therefore, considering these properties is crucial when designing a new drug product. Today several AI-based systems can predict the physicochemical properties of a drug product. For instance, machine learning uses a large volume of experimental data to train the program. Besides, drug design algorithms such as electron density, coordinates of atoms, and potential energy measurements can help develop feasible molecules and predict their properties.