Omar Gadir, Ph. D., Founder/CEO.
Pharma R&D Crisis
The pharma industry has entered a severe crisis. There has been a steady decline in revenue. One of the main reasons is the growing number of generic drugs, and the costly improvements needed for new drugs to excel over the generic ones. Some think the underlying problem is the persistent decline in pharma R&D productivity. A study by Scannell et al., estimated that “The number of new drugs approved per billion US dollars spent on R&D has halved roughly every 9 years since 1950, falling around 80 fold in inflation adjusted terms”. During this period the industry’s R&D model did not change. Unlike other industries, pharma companies still rely heavily on outdated manual procedures to perform data and scientific analysis.
Why The Pharma Industry Has Not Changed?
The reasons the industry resisted change are:
- They have been very successful and profitable for a long time.
- Pharma industry is a closed industry. Companies perform R&D the same way. A pharma researcher moving from one company to another cannot offer new ideas about how to make radical changes in the R&D process. In comparison, software companies are very different. When a software engineer changes jobs he/she brings new ideas about improving software tools, architectures and implementations. This what makes software industry adept to change.
- Biologists doing research in an academic institution perform the same manual procedures as biologists working for a pharma company. They join pharma companies with the same mindset.
Pharma’s Archaic R&D Procedures
For decades pharma companies have been using the same procedures for data and scientific analysis. Extracting insights from data is based on manually querying data sources, opening documents and reading them. This is slow and is error prone. Only a few out of up to millions of documents are processed. Some big pharma companies use their own tools for data analysis. The tools are mainly search engines and bioinformatics. The search engines deal with limited data sets. Bioinformatics is an interdisciplinary field combining molecular biology, mathematics or statistics, and computer science. Its main disadvantages are inaccessibility to most biologists, heterogeneity of how data are analyzed, annotated, and displayed.
Pharma’s Initial Solution To The Innovation Crisis
Initially pharma companies followed the steps of companies in other sectors and tried to solve the innovation crisis by:
- Mergers and acquisitions, consolidation, and licensing agreements.
- Outsourcing to improve drug discovery by relocating certain operations to areas where labor costs are cheaper.
- Embracing R&D collaborations to increase diversity and tap into the more creative and innovative cultures in small companies and academia.
- Form strategic alliances and active involvements in innovation networks.
- Strategic industry partnerships and cross company standardization.
The above solutions are not adequate. The rationale for their adoption is cost reduction. They do not make radical change to the R&D drug development process and for this reason the innovation crisis will persist. Mergers and acquisitions have been going on for a long time and they failed to alleviate the innovation crisis. According to Dr Jackie Hunter, in an article published by Drug Discovery World Journal,
“Thus 29 of the companies that existed in 1980 now have reduced to nine global pharmaceutical giants. However, this strategy (mergers and acquisitions) did not lead to the expected increases in new product approvals, nor did they lead to a reduction in costs per approval.”
Other suggested solutions include:
- Open source drug discovery: based on allowing everybody access to pharma results and products.
- Crowdsourcing in drug development: used to obtain services from a pharma professionals network.
Success of these solutions depend on transparent data sharing, bearing in mind pharma companies are fiercely protective of their data and IP.
Pharma Disruption Is Inevitable
Because of pharma vulnerability disruption is inevitable. One of the disruptive technologies is analytics and machine learning. Some companies have already started exploring how to use those technologies to improve R&D.
Pharma industry is one of the most, if not the most, data intensive industry. The data sources are scientific journals and other publications, Electronic Medical Records, genomics, screening data, clinical trial data, patents, FDA data and data generated within an organization such as documents, reports and results of lab work. Pharma data is grossly underutilized. Using data analytics and machine learning software to extract insights should benefit pharma R&D. In this respect pharma data analysis problem becomes a software problem.
The best implementation of data analysis is to empower domain experts to directly access the data and perform analysis themselves. This is the essence of democratizing data. Democratization allows all researchers to get the best out of the data in a few minutes.
The cost of scalable software infrastructure is very low and for this reason applying software solutions to pharma R&D brings significant reduction in drug development costs. Extensive availability of cheap powerful computing platforms, low cost of storage, low capital and operational expenses of cloud computing and the short time to deploy SAAS offer attractive options.
Software, including AI, Is The Only Solution To Pharma Data Complexity
Pharma data is the most complex data. It is voluminous and is increasing at a rapid rate. The complexity of the data is due to the following::
- Many biological processes are not understood.
- Many processes in the life sciences are extremely entangled, with multiple variables operating at the same time to produce different, unintended or unknown results.
- Ambiguities regarding gene/protein names are a major problem in the literature and it is even worse in the sequence databases. For instance, the breast cancer protein BRCA2 has 12 aliases: BRCA2, BRCC2, BROVCA2, FACD, FAD, etc. To add to the complexity, the tumor protein (p53) has 7 aliases and interacts with the above breast cancer protein in addition to more than 100 other proteins.
- Many acronyms that change over time.
To extract good insights needs powerful algorithms. Currently the extraction of insights covers a fraction of a percent of the total data. Even within that fraction of a percent many documents are filtered out. Also, different documents refer to the same entity using different names and acronyms, which makes it difficult to manually identify all relevant documents. Manually extracting the relationship between different data sets and understanding the entanglement in the data is prohibitively difficult. It is beyond the ability of the human brain. According to human cognitive research, the human brain cannot process more than four variables at a time. With the right algorithms, computers have no limit. They provide reliable and repeatable results. Analytics and machine learning can provide powerful predictive models for all stages of drug development.
Software investment: low-cost, high-return
In 2014, about $140 billion was spent worldwide on pharma R&D. If a very small fraction of the R&D budget is spent on software solutions, pharma industry will reap great benefits. It will lead to new treatments, better understanding of human biology, better analysis of side effects, and other factors that could be the difference between life and death. Iteru realized the importance of a software solution to improve pharma R&D based on analytics and machine learning . Its product is the only one based on empowering pharma domain experts.