20 Jun 2018 Blog
The Superhero of Clinical Analytics & Reporting – Statistical Computational Environment

With all due respect to the Herculean task undertaken by the Human Genome Project which cost over 3 billion dollars and spanned over fifteen years. The time needed to sequence the human genome was recently pared back to just 26 hours. Though I was not involved in this project directly, it is definitely an inspiration to a healthcare IT entrepreneur like me.

So, what is the point I’m trying to make?

I’m referring to the gap between 15 years and 26 hours. In the future, this gap may diminish to a few seconds, bringing down the costs too. This gap is disappearing with the proliferation of technologies that can facilitate super computations for drug discoveries in nanoseconds and a research-conducive environment that aids more as a library than a mere repository.

While faster computation at lower average cost is one good reason I could share to emphasize the need for a Statistical Computational Environment (SCE); crowdsourcing, singularity of diverse clinical data formats, regulatory compliance, reference for future research, accountability, auditability, transparency, extensibility and scalability are a few other reasons for the need and adoption of SCE.

Looking back, what can you expect out of an SCE?

Statistical computations own certain features that are beneficial for clinical trials processes to thrive and support existing and prospective research. A few top-of-the-mind examples of this are:

Analytical excellence: SCE available on the cloud allows you to integrate different analytical applications, within the system or with third-party solutions. It also prepares an environment for execution and control of different programming languages such as SAS, R, Python and so on.

Analytics requires good quality of sourcing which is best achieved in a controlled environment that supervises and administers all information via secure logins, audit trails, versioning and role-based privileges and policies.

Workflow optimization: Heterogeneity in data is now a well-known attribute of clinical trials. This is unavoidable due to the inherent substance of age, geography, and media used in a clinical trial.

SCE provides a global workspace with a workflow for review and approval across multiple data sources. It allows teams from across the globe to access data, run computations, collaborate and share information using a single system. This also facilitates the tracing of existing clinical data back to its source.

Data standard support and clinical data preparation: Any format does not work, when you are preparing for an FDA approval. Metadata formats and clinical data stacking as per CDISC models – including SDTM, SEND, ADaM and Define 1.0 and 2.0, and other extensible custom models are supported.

Regulatory Compliance: This is not just about supporting data and format management for regulatory approvals, but also about providing quick access to the user in creating instant data outputs for an adaptive and agile submission process.

Some features that I strongly believe an SCE should possess.

Metadata management

Transparency is one of the aspects offered by an SCE to a clinical trial. Complexity in clinical data repository hierarchies and its sharing patterns, demand the presence of a clinical data management tool that can expedite reproduction of most relevant clinical data and in the most preferred format. This process includes clinical data storing, indexing, cataloging and aggregating, and accessing during a search.

Controlled Environment

Apart from administration and security management, the controls within the environment also enable data version control across multiple iterations, limited access to users, role-based access , and context-specific permissions. This is made available across all knowledge workers in areas such as pre-clinical, clinical operations and medical affairs to drive global collaboration between internal team members, consultants, contractors and development partners.


Lack of extensibility outside an SCE is a reason attributed to high attrition rates in the latter stages of clinical trials. SCE to a greater extent supports the success of commercializing useful therapies and molecular formulae. Drug development can be optimized when clinical data is integrated with the formats in which a scientist can access it. Owning an XIS platform at the core, the platform users can extract data from an XIS Server as well as other XML or Web service enabled source using a web-based application.


Either in the case of classical pharmacology, forward pharmacology, reverse pharmacology or phenotypic drug discovery, scaling data and output swiftly is a factor yearned for.

Application of mass spectrometry for the elucidation of chemical structures from databases is a high-demand expectation of researchers and investors to make the most out of existing and approved chemical formulae. Implementation of Nuclear magnetic resonance spectroscopy (NMR) on existing molecular structures during a discovery process or clinical trial would lead to many other channels within the drug discovery process. SCE allows such branching out of discoveries, enabling researchers to add more value to a clinical trial.


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.”

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