Re-examine data from historic research to discover new applications. Traditional methods may not have captured all the complexity that AI can parse - or the data may indicate that new techniques and technology would be applicable to the data.

Gene analytics and editing can be sped up and delivered with high accuracy using AI. The potential for radical change in patient outcomes in oncology is one of the most interesting potential outcomes from applied AI.

Analyse relevant biomarkers to evaluate potential medical applications and outcomes. Identify which genes potentially cause which disorders to simplify diagnosis of patients and provide insights into the functional characteristics of the genetic mutation.

Predict outcomes from fewer or less diverse experiments to reduce R&D costs and time to market - potentially also mitigating cost and loss of life from animal experimentation

Predict how successful CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) will be at editing the targeted genome

During the initial phase of drug research and development, the target for a new drug treatment must first be chosen. The target molecule will be what the drug compound interacts with to get the intended outcome, often the treatment of a disease.

Analyse relevant characteristics and identify potential individuals to be recruited from a relevant population to serve in drug testing trials. These trials may be different stages in the drug development process.

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Identify drug compounds with current regulatory approval which could be used in new ways to treat other conditions. Machine learning assists by searching through existing research literature for known and inferred relationships

Once a drug target has been identified, the drug compound itself must be evaluated for safe use in living organisms before live testing can begin. This includes researching how the drug will be metabolised by the body and identifying potential toxic interactions and side effects.

Optimise pricing strategy for drug portfolio. Whilst this will likely increase portfolio yield there are reputational risks if mis-managed.

Optimise resource allocation in drug development using both internal and external (e.g. social media) data.

Analyse the data set (cost / benefit analysis) of potential development projects to assist with prioritisation of research effort and resource allocation in the product development process.

Optimise design of clinical trials, including label writing and patient selection

Identify target patient subgroups that are underserved or not diagnosed to support creation / deployment of a mitigation strategy

Identifying biomarkers for boxed warnings on marketed products. Drug labelling may contain information on genomic biomarkers and can describe issues such as drug exposure and clinical response variability, risk for adverse events, genotype-specific dosing, mechanisms of drug action, polymorphic drug target and disposition genes, and trial design features.

Optimise the testing and manufacturing processes to enable efficient turnaround and throughput for drugs to be trialled during the research and development phase.

Predicting drug demand in different geographies for different products. This will potentially be driven by factors from disease outbreak vectors through to socio-economic indicators or even media-driven consumption demand trends.

Combining medications can produce negative side effects - and potentially mitigate the positive impact. Issues for this include limited overlap case studies, decentralised information and unclear cause and effect. Using AI on appropriate data sets can uncover previously unnoticed correlations. Note that 11% of the US population claim to have used at least 5 medications in a given 30-day period.

Researchers are taking advantage of computational power to analyse vast amounts of data on drugs and their interactions. Enhancing the drug research process by advancing data mining, integration and analysis as well as testing can result in quicker drug and cure discovery for known diseases.