Pharma & Biotech
Use Cases
Newsfor Pharma & Biotech
Accelerate drug discovery by automating research stages and data integration and analysis
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.
Analyse biomarkers such as genes for medical potential
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.
Enable genome sequencing for personalised cancer treatment
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.
Identify and validate a molecule to target with a drug compound
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.
Identify candidates for trial recruitment
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.
Identify existing drugs for improvement
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Identify new therapeutic uses for existing drugs
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
Identify target patient subgroups that are underserved or underdiagnosed
Identify target patient subgroups that are underserved or not diagnosed to support creation / deployment of a mitigation strategy
Model out hypothesis of drug impact
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.
Monitor patient outcomes
Continual research and observation of the drug''s effects on patients after the drug becomes generally available
Optimise clinical trial design including patient selection
Optimise design of clinical trials, including label writing and patient selection
Optimise medical product launch strategy based on past launch or market data
Optimise medical product (devices, robots, drugs etc) launch strategy based on past launches and relevant data
Optimise pricing strategy for drug portfolio
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 disease trends and other data
Optimise resource allocation in drug development using both internal and external (e.g. social media) data.
Predict biomarkers for drug box labelling
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.
Predict drug demand in different geographies for different products
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.
Predict outcomes from fewer or less diverse experiments to reduce research costs and time to market
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 potential adverse effects when drugs taken are combined
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.
Predict target drug resistance
Analyse candidate patient data to measure probability of individual resistance to deployed drugs, typically projected over time and across a broader population.
Predict the behaviour of CRISPR for gene editing
Predict how successful CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) will be at editing the targeted genome
Prioritise research and development projects
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.
Produce drugs for scaled testing
Optimise the testing and manufacturing processes to enable efficient turnaround and throughput for drugs to be trialled during the research and development phase.
Re-examine data from historic research to discover new applications
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.