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I keep seeing more and more headlines which report on genome-wide dissection of genetics, high-throughput technologies, genetic modifications and other biomedical breakthroughs in drug development. All of them lead to one common catalyst — a data-intensive drug development and discovery model used by the successful team. But, such a model can only thrive in the presence of a sound Statistical Computational Environment (SCE).
To me, two elements drive the success of data management during clinical trials. One, is the ability to bring diverse clinical 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 clinical data forms interferes with security, accessibility, traceability and auditability 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 on regulatory compliance.
NCBI also identifies that the publicly available biomedical info`1rmation 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 clinical 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 models as an inhouse capability, I also see it as a competitive factor for R&D units and scientists eager to hit the market.
Here is why it is competitive. It has led to virtual drug development models that allow scientists to test and analyze a molecule even before the formulation stage. Eliminating challenges in the preclinical stages can reduce drug 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 development and discovery.
To establish this approach, pharmaceutical and healthcare companies involved in drug development need to invite an integrated clinical data management approach. This involves defining clinical data sources for reliability and consistency, building a robust clinical data mining and warehousing infrastructure, and processing different insights useful to various stakeholders of the project and data users. Among these, McKinsey has pointed out that the first step itself takes about a year to complete.
Having been involved with multiple integrated clinical development platform projects, I have seen that drug-to-market lead time was brought down when the traditional drug development model was transformed into a data-intensive and technology-enabled environment that is integrated and automated to allow establishing focus on clinical data analysis and avoid mundane data processing tasks.
To gain a competitive edge, more and more successful drug development companies are moving towards and thriving in a data intensive drug development & discovery environment. Are you using a data-intensive drug development process or still stifled by the challenges of a traditional approach?
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.”