Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Abstract

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

Keywords: PBPK; QSAR; artificial intelligence (AI); dosage form testing; drug discovery; formulation; machine learning; pharmacodynamics; pharmacokinetics.

Figure 1

Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry.

Figure 2

Different supervised and unsupervised AI learning models/tools for pharmaceutical applications.

Figure 3

AI contribution to drug development and research. AI can be used to enhance nanosystem design, expand the present drug testing modeling system, and increase the accuracy of parameter and factor selection in drug design, drug discovery, and drug repurposing methods. It also helps to better understand the mechanism of membrane interaction with the modeled human environment by studying drug permeation, simulation, human cell targets, etc.

Figure 4

Application of AI tools in the pharma sector. AI tools are helpful for the analysis of multilayered data. Automated AI systems are used to perform effective searches, simulations, and refinements of data and processes involved in research and product development. The system biology database, chemical database, genomic database, phenotypic database, and AI bots are used for better exploration of drug models, drug release, and activity predictions along with recommendations for effective drug delivery systems.

Figure 5

AI for Oral Dosage Forms. Conventional tablet analysis is performed by screening many factors, such as drug release, drug loading, and study of the tablet geometry and hardness, by using in-process quality control tests along with ultraviolet spectrophotometry. These methods are often time-consuming and cost-ineffective to the industry. To address these issues, the combination of such traditional techniques along with AI was performed by using ANN, SVM, PAT, and regression trees. The data analysis and drug release predictions indicated that particle size distribution was a crucial factor for the same. Defective tablet surface crack analysis is performed by XRCT in combination with AI, containing three modules for distinguishing features for effective application in the healthcare sector.

Figure 6

AI can contribute to protein development and customized biologics by using data analysis, predictive modeling, and pattern recognition tools for better improvisation in the protein development process and customized proteins. Knowledge of altered biological pathways and finding disease targets are required for the same. The prediction of protein folding from amino acid sequences and the use of deep learning and molecular dynamic simulation for better understanding can be performed by AI. The prediction of protein/peptide binding affinity and toxicity studies can be performed effectively by AI with the help of SAR and toxicological datasets.

Figure 7

Role of AI in PKPD studies. Pharmacokinetic studies include absorption (A), distribution (D), metabolism (M), and excretion (E) studies. A pharmacodynamic study includes the drug’s effect on the target. Understanding the effect of drug molecules and their distribution requires a large number of calculations. A smaller miscalculation or missed dataset may lead to a huge error that may be critical. AI helps to accelerate complicated calculations without missing datasets and provides more accurate, faster, and cost-effective results. It converts complicated data into easily understandable and representable graphs, which might help to identify the root cause of the problem. It can also help to minimize animal studies by calculating the impact of different conditions such as enzymes, diseased conditions, dosing differences, patient data, etc., in different animals and reduce the number of animals required for clinical trials.