Data, AI and Machine learning in Life Sciences

Advancements in the technologies behind Data, Artificial Intelligence (AI) and Machine learning have had an impact on every industry, as our reliance on them grows ever larger. This rings true for Life Sciences, however the take up in this industry has been somewhat slower than in others (Finance, Marketing etc.). Here, we look at these areas and how greatly they could, and in some cases are, revolutionising the approach to Life Sciences.

Data:

Pharmaceutical and Biopharmaceutical companies inherently accrue and collate a massive amount of data, in the forms of research and development results, patient records etc. This data requires a massive amount of storage and resources to manage, but also presents a huge opportunity for Life Sciences companies.

The evolution of this “Big Data” means that there are petabytes of EMRs (Electronic Medical Records) providing huge sets of data ready for mining. After the initial and crucial data centralisation (the process of collating records into a “machine-readable” format for analysis), this data can be used by companies to “gain insights… into how their therapies are performing at a more granular level” (Forbes). This analysis of patient symptoms, genealogies, reactions etc. can help provide accurate insights into the efficacy of a medicine, making it easier for companies to justify the worth and cost of their product. This could prove a crucial time advantage in the increasingly saturated and competitive pharmaceuticals market.

AI & Machine learning:

Once centralised, this data needs analysing. AI and Machine learning provide a way of gaining previously unattainable insights, trends and analyses from large sets of data. It can also be done using a fraction of the time and resources as a manual process would – compare clicking a button and receiving results instantly, to a group of people trawling through thousands of documents. These new techniques for analysis can be applied to several areas within Life Sciences, including:

·         Accelerating development process – faster attained, actionable results will help speed up processes such as R&D and clinical trials. This means the (sometimes 10+ year) process of getting a drug product to market can benefit from some much-needed time saved. Being able to analyse data quickly within multiple parameters will make process areas such as compliance, quality, risk management and manufacturing .

·       Optimising spend and pricing – identifying trends in processes will have a massive impact on quality and compliance and can therefore be used to cut costs and save money. Analysing errors and wastage will mean better yields in manufacturing as well as less wastage in the manual processes. Additionally, machine learning can be utilised in areas outside of production to make processes leaner, such as sales & marketing or finance. Predictive analytics and trend analysis can also be used in setting pricing models for products, predicting consumer spending habits through predictive modelling.

·         Patient suitability studies – having thousands of data points per patient analysed as efficiently as possible will mean that choosing suitable patients for clinical trials becomes a much easier and more accurate process. Having a large set of EMRs studied and judged for feasibility at speed and with less errors than a manual process will mean a faster procedure with less risk of harmful unforeseen errors.

Data, AI and Machine learning have the potential to completely change how the life sciences industry conduct research and develop their products, causing the same level of disruption and progression as the discovery of molecular biology in the ‘70’s and the Human Genome project in the ‘90’s. Life sciences and pharmaceuticals are no strangers to rapid growth and development however, and are already starting to embrace all that these new technologies have to offer.

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