How Artificial Intelligence Impacts The Drug Development Process
Artificial intelligence (AI) has proven its power and usefulness by contributing to many advancements in many industries, including the pharmaceutical industry.
Drug discovery and development cost a huge amount of resources and time. Medicine research and development (R&D) are estimated to have an average cost of billions and take more than a decade before a final product is proven safe for human consumption. Due to the drastic increase in digitization of data, AI solved the efficiency of acquisition, analysis, and application of knowledge from clinical studies. Now, it seems easier to develop and create new drugs from scratch with the aid of AI technologies.
Many global biopharmaceutical companies utilize AI and other related sterile technologies in their operations. They employ machine learning to power up their discovery of immuno-oncology drugs. Also, they make use of various start-ups whose main product involves AI in finding metabolic-disease therapies and curing cancer. Per experts, AI can usher society into an era of cheaper, efficient, and quicker drug discovery.
But how exactly does AI impact the process of drug development? Keep on reading to learn more.
AI For Designing Drug Molecules Efficiently
Creating and developing a drug molecule requires predicting the target protein structure. This is essential to assign the correct target for a successful treatment. Many proteins involved in developing a disease are usually overexpressed proteins. AI can help simulate the structure of the target protein site to design the molecule. The prediction of the protein target site, as well as the effect of the drug on it, can ensure the safety precautions of the synthesized drug before manufacturing.
AI For Drug Screening
Discovering and developing new medicine can take at least a decade and it costs a large amount of money on the part of the medicine company. In most cases, 90% of the therapeutic molecules fail Phase II of clinical trials and regulatory approval. With the help of AI that uses algorithms, the feasibility of the drug synthesis becomes heightened as well as the prediction of the particular drug’s toxicity and in vivo activity.
AI-based tools can also help predict the drug’s physicochemical properties, such as solubility, intrinsic permeability, and others. The machine learning involved in these AI tools uses large data sets from past optimizations to train the program. AI can also be used in measuring the binding affinity of the drug. Drug target binding affinity (DTBA) is crucial in predicting the potential drug interactions that will affect the consumers.
AI For Effective Clinical Trial Designs
Clinical trial sessions are conducted to establish the safety and efficacy of a certain drug for human consumption. But out of 10 molecules, only one passes the phases of clinical trials, which results in a massive loss for a pharmaceutical company. With the vast potential of AI implementation, failures resulting from unsuitable patient selection and insufficient technical requirements can be reduced significantly.
AI can assist in choosing patients for recruitment from a specific diseased population. It can also employ its machine learning techniques in the preclinical discovery that helps in the early prediction of the lead drug molecules that are likely to pass the clinical trials based on the chosen patient population.
AI-based Methods Instead Of Trial And Error
AI is turning the drug development process upside down. By replacing the conventional trial-and-error approach with patient-driven biology and data. To come up with a better framework for drug synthesis. One area in discovering a novel drug molecule is determining the suitable dosage for a drug to do its job. AI-based methods solved this dilemma by employing computational tools in formulating design areas. The design area is responsible for ensuring there are no stability issues, porosity, and dissolution in the drug.
Looking Forward
Experts regard AI will undoubtedly hasten the creation and review of novel drugs and reduces the costs and time to be safe for human consumption. Yet the real success of it still depends on the substantial amount of data to train the AI system. Pharmaceutical companies still need to access these data sets from various database providers like hospitals to ensure accurate result prediction.
Human intervention is still mandatory for the successful implementation of AI platforms. Other challenges might arise in adopting AI technology in pharmaceutical companies due to lack of personnel training, limited budget, and the like. Despite these mentioned lapses, AI will surely help pharmaceutical companies automate specific tasks in drug development, clinical trials, manufacturing, and sales. It can contribute to the optimization of old batches of drugs as well.
