By Naveen Joshi, Allerin, Mumbai

The process of manufacturing a drug, from molecule to market, is expensive, high risk and long, taking 10-15 years to complete. AI in pharma can process millions of data points to rapidly discover, test and commercialize therapeutics. Using AI, pharma companies can save on R&D costs and take drugs from laboratory to market faster.

Pharmaceutical and biotech companies are increasingly adopting artificial intelligence to automate various steps in the drug manufacturing process. To enable the implementation of AI in pharma, companies have created a unified repository of data collected from clinical trials, drug discovery databases, patient statistics and many more sources. Using this vast amount of data, artificial intelligence and machine learning algorithms can model diverse root causes for diseases, identify drug candidates, design and optimize those drug candidates and test their efficiency through simulations. In the traditional methods of drug discovery, researchers discover new drugs through testing existing medicines that had unanticipated effects, testing the impact of molecular compounds against many diseases and identifying the biological target for a particular disease. Once pharmaceutical scientists have identified the promising compound, the drug development phase begins. This process from drug research to commercialization costs companies $2.8 B and takes over 12 years. AI in pharma can help streamline the drug-making process, drastically bringing down the cost and time for drug research and commercialization.

IMPACT OF AI IN PHARMA DRUG DISCOVERY

The traditional path of drug development is lengthy, expensive and suffers from high failure rates. During the drug discovery phase, pharmaceutical scientists test millions of molecules, but only a handful of compounds progress to preclinical or clinical testing. The chemical space of pharmacologically active molecules contains around 10⁶⁰ organic molecules. This vast chemical space serves as the source for drug discovery and scientists so far have been able to synthesize only a fraction of these compounds in the lab. The sheer size and complexity of exploring and identifying useful molecules in this space take a huge amount of time and effort.

As AI can process millions of datasets in a fraction of time, it is the perfect technology for drug discovery. AI and deep learning algorithms can speed up the process of identifying correlations in pharmaceutical datasets and find new connections that pharma researchers could not see. Machine learning algorithms are trained on biomedical datasets to detect patterns and trends relevant to human biology. AI applied to drug discovery can rapidly explore immense chemical space and generate molecules that can be synthesized in the laboratory. AI can analyze and tailor chemical properties more thoroughly and quickly than a team of scientists using traditional methods.

Accelerated Drug Development

One of the phases of drug discovery, hit identification analyzes the drug information bank to find pre-existing compounds that could be active ingredients in new drugs through a trial-and-error process. Using AI, hit identification can be automated through deep learning models that will automatically come up with the multitude of combinations of the molecular data and assess the biological performance of every combination in treating certain diseases. Thus, AI can speed up the process of identifying and designing the structure of the lead compound in drug discovery. The team of researchers can then leverage these deep learning algorithms to select the drug candidates and test them in the pre-clinical and clinical trials.

AI can also identify disease features that are not usually detected because only a single hypothesis is being tested. AI will enable pharma researchers to evaluate potential treatments against multiple targets at the same time. All this will lead to drugs being brought in faster for pre-clinical and clinical testing phases. This level of progress takes years using the traditional, non-automated techniques, but AI can complete this for one or two compounds in a matter of weeks.

Drastic Reduction in R&D Time And Cost

Depending on the complexity of the disease and treatment, the cost of manufacturing one drug is between $2.6B and $6.7B. From initial proof-of-concept to commercial launch, pharmaceutical companies take about 12 years to develop and commercialize a drug. AI can significantly bring down the time to identify potential drug candidates, conduct clinical trials, pass regulatory standards and bring the drug to market. AI could reduce the drug discovery costs for companies by as much as 70%.

Many healthcare startups and leading pharma enterprises are adopting AI to slash research and development. An England-based AI startup found a drug candidate for obsessive-compulsive disorder in less than a year as opposed to the typical period of 4 years. Dramatically shortening the drug discovery and development time and costs will also help control medicine prices and make them more accessible to an extensive customer base.

Enhanced Patient Care

AI in pharma drug discovery can help identify novel potential treatments against new disease targets for untreatable diseases like lupus, cancers and fibrotic diseases. Applying artificial intelligence to drug development can lead to effective monitoring of drug efficacy and safety. AI-powered insights can be used for analyzing adverse effects of the treatment and identifying patterns to understand which target population would benefit from the treatment and which population segment should avoid the treatment. By applying artificial intelligence to drug discovery, the technology can help not only in the creation of effective drugs but also increase the effectiveness of existing drugs.

The healthcare and pharma industries are on the brink of a large-scale disruption driven by vast pools of interlinked data, open platforms and innovation in AI and machine learning. Besides drug discovery, the pharma industry is adopting AI applications to optimize clinical trial design, effectively monitor patients enrolled in clinical trials, predict seasonal illnesses and pandemics and detect defects in drug manufacturing. AI in pharma demonstrates great promise to accelerate drug research and development and provide options for diseases with no available treatment. With AI technologies, pharma can make drugs faster, cheaper and better. Healthcare and life sciences enterprises need to combine their drug research and development with artificial intelligence to remain competitive and future-proof their businesses.