Top Applications of AI in Pharma

In 2017, the US Food and Medicine Administration (FDA) approved 47 novel molecular entities (NMEs) into the market, bringing breakthrough drug approvals to a 21-year high. Although pharmaceutical R&D has never been better, bottlenecks are still being caused by several toll gates in the form of manual decision-making phases across the product development lifecycle.

The cost of finding an NME can reach up to USD 2 billion or more when lab work, trials, and regulatory paperwork are added together. When the drug is ready to hit the market at the end of a decade, businesses are compelled to charge prices that are out of reach for the majority of consumers, particularly in low-income nations.

Every seven months, the amount of data produced by genomics research doubles, and this data includes crucial dosage and formulation details as well as trial safety signals. This flow cannot be managed by the current IT infrastructure, let alone stored and shared securely. This is made more challenging by compliance regulations, such as those that the General Data Protection Regulation (GDPR) just added.

Human Genome Decoding

Some organizations have noticed this issue and are creating cloud-based systems as clinical and medical data repositories with built-in analytics capabilities. It is anticipated that these technologies will assist researchers in establishing a single source of truth (SSOT) for guiding the entire drug development process. These platforms will eventually serve as the building blocks for machine learning (ML) and artificial intelligence (AI)-driven process automation systems for assisting clinical choices and forecasting the efficacy of a new treatment even before it reaches the trial stage.

AI in pharma significantly expands the amount of data that can be studied during the drug discovery and testing process through context normalization. It may then produce and objectively test fresh hypotheses at a rate that is unfeasible for human researchers. Companies can not only speed up decision-making at every level by visualizing the resulting insights through a consolidated dashboard, but also obtain total visibility throughout the R&D value chain.

Using dynamic visualizations, some AI platforms may evaluate private data such as lab reports and assist researchers in finding possible connections between drug activity and disorders. In vivo simulations that help identify compounds for moving ahead to the live testing phase can further reduce the requirement for conducting animal research once an AI-based solution like this is fed with enough historical data from prior and ongoing trials. Additionally, it will be able to identify safety warnings that would have been overlooked in the continuous flow of data produced during Phase II trials.

Speeding Up Drug Discovery

The applications of AI in pharma don't stop there. It can produce and validate a pipeline of clinical hypotheses, which can greatly improve R&D success rates when combined with natural language processing (NLP) and deep learning algorithms. Compared to a conventional lab researcher who can only able to manufacture roughly five of them in the same amount of time, a platform created by BenevolentBio can generate as many as 36 and validate 24 of them in vitro.

In terms of what AI in medicine may accomplish in the field of biological sciences, this is merely the tip of the iceberg. Companies are now able to collect clinical data from patients while they go about their daily lives because of the consumers' growing acceptance of wearables and mHealth apps. Predictive criteria can then be used in conjunction with this real-time data input to determine a drug's effectiveness, a patient's likelihood of dropping out, and the outcome of a trial. Within one system, clinicians can concurrently study a patient's medical history and possibly even get lab results.

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