Neal Singh, COO of software company Icertis discusses how technology, in particular AI is helping to fast track drug discovery within research and development (R&D).
Neal Singh, COO Icertis
The pharmaceutical industry is undergoing a period of rapid change. Merger and acquisition activity is high, regulations are becoming more stringent, and companies are forced to be evermore focused on cost. Regulation challenges were recently made more complex by the introduction of GDPR and uncertainty in Europe around Brexit.
One area of pharma that is particularly impacted is R&D. Between 2010 and 2017, the average cost to develop a pharmaceutical compound, from discovery through launch, rose by 82% from $1.19bn to $2.17bn. Spending can be reduced by speeding up time to market, but with multiple legal hurdles to jump before a company can generate revenue, working quicker can be difficult.
We are however starting to see digital technology revolutionise aspects of the pharmaceutical industry. Technology has the potential to disrupt everything; from the way trials are conducted to how medicine is manufactured and delivered. One digital initiative being undertaken at many of the world’s biggest pharmaceutical companies is enterprise contract management.
Pharmaceutical operations involve relationships with a mix of external stakeholders, which are typically defined by contracts; none more so than R&D. A pharmaceutical company’s relationship with partners is business critical, so simplifying and automating processes such as contract negotiations adds significant value. Missed entitlements, unwanted expiries and renewals, and non-compliant clauses can damage important business relationships, slow down progress and consequently cost money.
Manual processes for managing contracts can make visibility into complex supply chains impossible – particularly when contracts are organised ‘back-to-back’ (whereby a vendor contracts with another vendor to fulfil an obligation) - and expose companies to regulatory liabilities.
Clinical trials are a central aspect of R&D in pharma and cause some of the greatest delays. According to the Association of Clinical Research Professionals, contract and budget negotiations are responsible for 49% of study delays. The axiom” time is money” rings true.
As a result, a function that enables sponsors and Contract Research Organisations (CRO) to manage clinical agreements and track budgets as part of a trial is likely to deliver significant value. By using technology to better manage contracts around clinical trials and R&D work, pharmaceutical firms can better ensure compliance, improve visibility and collaboration with CROs by enabling greater access to contracts, and more easily track budget consumption. Introducing contract management can reduce contract cycle times by up to 80%, dramatically increasing time to revenue.
Today’s pharmaceutical firms are facing a growing number of business challenges and must find ways to accelerate their operations. By protecting against regulatory risks and speeding up traditionally slow and complex R&D processes, there exists an opportunity to improve time to revenue and ultimately, the delivery of crucial medicines to patients.
Better visibility across an organisation can also directly save money by identifying where multiple departments are contracting with the same vendor. For example, different development teams might be sourcing the same services separately from one supplier. By recognising where these overlaps exist, a company can use this information to ensure consistent terms and therefore reduce costs.
By leveraging the latest technologies in artificial intelligence (AI), machine learning and the cloud within the parameters of a simple and easy to use contract management system, pharmaceutical firms can unlock R&D bottlenecks and be more agile and better prepared for the challenges the industry faces. Cognitive Services can be used to stitch together several cognitive abilities (Text Analytics, Bing Entity Search API, Translator Text API and others) to infuse AI within contracts. Machine Learning for Text Analytics can be applied to train AI models. AI can be used for intelligent clause identification and attribute extraction to reduce the time to digitise legacy and third-party contracts by up to 80%, improve post-execution contract compliance by up to 90%, reduce cost of compliance by up to 60% and discover new revenue opportunities in legacy contracts.