Data, a rising mindset and methodology for drug development

By Suvarnala Mathangi | Date: June 30, 2018 | Blog | 0 Comment(s)

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I have been discovering more and more headlines reporting about genome-wide dissection of genetics, high-throughput technologies, genetic modifications and other biomedical breakthroughs. All lead to one common catalyst — a data intensive drug discovery model used by the success team. But, such a model only thrives in the presence of a sound statistical computational environment.

To me, two elements drive the success of data management during clinical trials. One, is the ability to bring diverse data forms in a single comprehensible format and the other is data driven in-depth knowledge to evaluate and validate the drug concept. The variety of data forms fiddles with security, accessibility, traceability and audibility which otherwise empower a clinical practitioner with better insights and adherence to regulatory compliance. These factors can be achieved by introducing the drug into a data driven environment, supported by a team with a similar data-intensive mindset who can build a statistical computational environment (SCE). Using SCE, customers can establish control, auditability, traceability and gain efficiency without compromising regulatory compliance.

NCBI also identifies that the publicly available biomedical information is the basis for identifying the right drug target and creating a drug concept with true medical value. This has also helped them enhance their understanding about the pathophysiological mechanisms of diseases with the help of a data driven development model.

Prediction of metabolism, application of translational bioinformatics in reporting long-read amplicon sequencing of chronic myeloid leukemia and multi-drug resistant bacteria, and gene mutations are results of a data-intensive model. Apart from looking at data-intensive drug development model as an inhouse capability, I also see it as a competitive factor for R&D units and scientists to hit the market.

Here is why it is competitive. It has led way to virtual drug development models that allow scientists to test and analyze a molecule even before the formulation stage. Eliminating challenges in the pre-clinical stages can reduce development costs and the lead time taken by each formula to transform into a druggable output. Nature, a popular science magazine has confirmed through its research that the unprecedented challenges of a pharma business model can be met by shifting investments to the earlier stages of drug discovery.

To establish this approach, pharmaceutical and healthcare companies involved in drug development need to invite an integrated data management approach. This involves defining data sources for reliability and consistency, building a robust data mining and warehousing infrastructure, and processing different insights useful to various stakeholders of the project and data users. Among these McKinsey has pointed that the first step itself takes about an year to complete.

Having involved with multiple integrated clinical development platform projects, I have seen that drug-to-market lead time was brought down when traditional drug development model was transformed into a data-intensive and technology enabled environment that is integrated and automated to allow establishing focus on data analysis and avoid mundane data processing tasks.

To gain the competitive edge more and more successful drug development companies are towards thriving in a data intensive drug development & discovery environment. Are you on the banks of Data intensive drug development process or stifled by the challenges of a traditional approach?

MaxisIT

At MaxisIT, we clearly understand strategic priorities within clinical R&D, and we can resonate that well with our similar experiences of implementing solutions for improving Clinical Development Portfolio via an integrated platform-based approach; which delivers timely access to study specific as well as standardized and aggregated clinical data, allows efficient data quality management, clinical reviews, and statistical computing.

Moreover, it provides capabilities for planned vs. actual trending, optimization, as well as for fraud detection and risk-based monitoring. MaxisIT’s Integrated Technology Platform is purpose-built solution, which helps Pharmaceutical & Life sciences industry by “Empowering Business Stakeholders with Integrated Computing, and Self-service Analytics in the strategically externalized enterprise environment with major focus on the core clinical operations data as well as clinical information assets; which allows improved control over externalized, CROs and partners driven, clinical ecosystem; and enable in-time decision support, continuous monitoring over regulatory compliance, and greater operational efficiency at a measurable rate”.