Digitizing The Pharma Research Value Chain For Faster Drug Discovery Through Precompetitive Collaboration
Contributed commentary by Abhishek Shankar
August 26, 2016 Contributed Commentary | The global pharmaceutical industry is at a crossroads. The average development cost per approved product has soared to a whopping $2.6bn, threatening the sustainability of the blockbuster model of drug discovery and manufacturing championed by Big Pharma.
Looming patent cliffs, combined with mounting competition from makers of biosimilars and generics, and stricter regulations, risk significantly eroding drug manufacturers’ margins. With over 90% of the drugs that reach the clinical testing stage failing, the typical time to market for new medicines has increased to anywhere between 10 and 15 years.
A major factor contributing to this challenging business environment has been the below-par efficiency and productivity of pharma companies’ research & development (R&D) function. Most firms have reaped disappointing return on investment (ROI) with regard to their drug discovery initiatives, despite R&D accounting for almost 16% of overall industry revenues.
It is, therefore, imperative for pharma organizations to reimagine their research value chain, in order to accelerate product innovation, and boost molecule pipelines during the discovery stage.
Case for Precompetitive Collaboration
One tangible way pharma companies can optimize their R&D function is by partnering with peers, contract research organizations, academic institutes and government entities for drug discovery. Precompetitive collaboration on this front could potentially facilitate a 30% reduction in product build costs, as well as shorten development timelines by at least 10%.
Adopting such an approach can also help drug manufacturers bring down the probability and cost of clinical failure, through identification of compound-related liabilities early during the research lifecycle, while delivering better patient outcomes.
The last few years have witnessed the formation of multiple consortia involving public and private entities that are specifically geared toward translational research, with a view to fostering commercialization of new medicines.
The Innovative Medicines Initiative (IMI) has been supporting collaborative research projects, and promoting networks of industrial and academic experts. The Pistoia Alliance, representing several life science companies, vendors, publishers, and academics, remains focused on addressing issues around aggregation, access and sharing of research data.
Other notable undertakings in this regard include the Center for Therapeutic Target Validation (CTTV), Coalition Against Major Diseases (CAMD), Structural Genomics Consortium (SGC) and Serious Adverse Events Consortium (SAEC).
Historically, precompetitive collaboration in the sector has focused on identification and corroboration of predictive biomarkers, preclinical safety and toxicology, and development of tools for target validation.
In recent times, though, optimization of compounds during the early discovery phase has emerged as a key trend. The IMI-backed European Lead Factory provides researchers, enterprises and patient organizations with free access to 500,000 novel compounds, aiming to deliver unique starting points for drug discovery.
Meanwhile, AstraZeneca and Roche have begun sharing data on compound fragments to determine how fragment structure influences a drug's efficacy and safety. This, the pharma companies hope, will enable them to expand the range of compounds against which they can screen their targets.
Data pooling is another significant focus area, as highlighted by the ongoing aggregation of findings from various genetic studies seeking to correlate genotype with phenotype for target validation. The IMI’s NEWMEDS project has facilitated improved, cost-effective and faster design for trials involving schizophrenia drugs by collating relevant data from previous clinical evaluations.
Finally, there has been industry-wide consensus on the need to enhance the efficiency of clinical trial processes. Ten leading drug makers have come together to form TransCelerate Biopharma, a nonprofit joint venture tasked with standardizing various aspects of these processes.
But impeding precompetitive collaboration is the poor end-to-end management of research data. The bulk of drug manufacturers’ existing IT landscapes do not facilitate standardized aggregation, storage, and annotation of relevant data, thereby hampering sharing. Many companies also lack requisite tools, including web services, for institutionalizing effective data access and mining.
And, to make matters worse, prevalent legacy systems entail high total cost of ownership (TCO), with maintenance and upgrades of the former accounting for up to 80% of pharma companies’ IT-related operating expenditure (OPEX).
Here are some pointers for pharma companies to keep in mind when revamping the technology landscape underpinning the research value chain:
Leverage cloud: Cloud computing can enable faster, yet cost-effective, product innovation by offering a collaboration platform for drug companies. Pharma organizations should harness the research cloud to harmonize and delegate transactional processes to third parties, and concentrate on clinical trial execution to address fundamental biomedical questions. Apart from helping reduce maintenance and operations (M&O) costs related to IT infrastructure, such a next generation research cloud (NGRC) can drive process improvements and data standardization. And, going by Gartner’s estimates, consistent workflows and data federation can translate into annual cost savings in the range of $5.8bn to $6.6bn for the industry.
Implement plug-and-play platform: Rather than setting up independent, complex IT systems, firms can adopt an open standards-based, plug-and-play platform to integrate their respective core IT capabilities. Rollout of such a flexible platform will allow various stakeholders in a given alliance to reuse each other’s R&D assets effectively. Companies can cover a wide range of research-related functions–such as target identification and validation, lead generation, purification and characterization, screening, and drug design and synthesis–by implementing this setup.
Define standards for data management: Pharmaceutical organizations must agree on a common architecture for robust data aggregation, exchange, analytics and governance across their disparate laboratory platforms. Standards-based data management can pave the way for informed decision making and development of reusable models for pipeline maximization.
Abhishek Shankar is Industry Head, Life Sciences Americas at HCL Technologies, responsible for driving the practice's profitable growth through increased mindshare. Over the years, he has overseen the successful design and implementation of IT transformation initiatives for clients across biopharmaceutical, medical devices and other industry segments. He can be reached at firstname.lastname@example.org.