Pretty much every article starts with the challenges that pharmaceutical industry across the globe is facing. It is a difficult industry and everybody acknowledges that, considering the time to develop an original drug (10-15 years), the costs involved (last time I checked it was USD 2-3 billion), the high attrition rates of drug candidates (1 out of 5,000 or 10,000 leads make way for FDA approval), the tough regulatory environment which is varied across countries and geographies, and the rising pressures on pricing (pricing advantage for truly outcome-driven therapeutics). All of these, with the looming patent expiry, the imminent entry of generics, and the tantalizing RoIs, make it even more difficult.
Pic Credit: CreativeCommons.org
Well, technology is here to rescue. It will be a grave injustice to talk about technology implementation in pharma R&D in just one article, and because AI and ML are the current buzzwords, I thought, maybe, we could specifically discuss the role Artificial Intelligence plays in streamlining and improving success rates of pharma R&D. Before we go any further, let us have a quick look at the drug discovery process and the typical timelines associated.
More often than not, it takes more than a decade (sometimes > 15 years) for a new drug to enter the market. One should not forget if technologies and in-silico modeling are not effectively used in early stages of drug discovery, drug failure at a later stage is a significant waste of time and money. Now, pharma companies have already been using computational tools to conduct ADMET predictions and in-silico modeling, so what new has Artificial Intelligence to offer?
To answer this question, important will be to look at some of the AI initiatives and activities of key pharmaceutical companies. You may find initiatives where pharma companies are scouting for AI innovation via their open innovation program, so if you are a tech start-up, think about it.
The list is not exhaustive and is only for the purpose of illustration, but one can clearly see that leading pharma companies have already dipped their toes in Artificial Intelligence-mediated drug discovery and development.
Going back to the drug discovery process diagram, let us superimpose the AI-applications across the process.
Artificial Intelligence and Machine Learning algorithms, not only simplify the existing tasks/processes, it saves time, while adding significant value. Let us understand this better.
BioXcel Corporation, a biopharmaceutical company is working on integrating big data and AI into drug discovery process. One such product is EvolverAI which uses AI algorithms for drug discovery to find the best therapy, thus reducing drug failure. Evolver AI uses big data to screen through huge volumes of structured and unstructured data related to genes, proteins, disease pathways, targets, symptoms etc. within the field of Neuroscience. This is followed by creation of meta-data, which contains network-maps, linking pathophysiology of diseases with drugs. These meta-data are then fed into a decision matrix, which compares all the drugs. Using AI algorithms and human intelligence, several hypotheses are built based on the known linkages, of which the best hypothesis gets selected for future experiments and clinical trials, significantly reducing both time and risk of drug failures.
Exscientia, which has partnerships with both GSK and Sanofi, uses AI to learn best practices from drug discovery data, and helps researchers generate drug candidates in much lesser time.
The beauty of such algorithms is in their ability to go though various data-sets – from medical records, publications, clinical trial data, available data on disease pathways and drug-disease correlation to derive meaningful analysis that can help researchers in decision making. Similarly, screening through EHR, current and past clinical trials, and available publications on epidemiology, can help in site selection for clinical trials. Applications are many, and this is still an evolving area, with many developments happening in the stealth mode.
Many diseases such as cancers and neurodegenerative disorders, having high mortality and morbidity, have been debilitating for people across the globe, with pharma companies pouring in significant amount of money, yet with not-so-significant success rates.
Take Alzheimer’s for example, after three large clinical trials for solanezumab, which was called the breakthrough drug candidate, it hasn’t been able to display significant change in patient’s conditions as compared to a placebo. Solanezumab is an anti-Aβ mAbs, that targets Amyloid beta, which is one of the main components of the amyloid plaques found in the brains of Alzheimer’s patients. Does that mean researchers are looking in the wrong place? Is there a need to rethink the disease pathway and establish newer biomarkers to be targeted? THESE are the areas where AI and ML can help.
Similarly, increasingly the drug research community is realizing that not all drugs work on all patients. AI algorithms can help stratify patient population to identify what causes drug efficacy in some patients while nothing really in others – could be due to an aberration, mutation, any specific biomarker – and AI can help recognize the same in the most efficient manner possible.
And that is not all, there is a lot more – we are just scratching the surface, but one cannot deny that these technologies are the next hope for pharmaceutical companies. What do you think?
Source: Nature, Wall Street Journal, MIT Technology Review, Forbes, PR NewsWire, Micar21, SlideGeeks, BenchSci, Xconomy, MedCityNews, Company Websites
The article was first published on Ms. Manishree Bhattacharya’s LinkedIn Pulse Blog, here. Its been republished here with the authors’ permission.
Independent consulting – strategic research, industry analysis, healthtech evangelist, digital thought leadership (ex-NASSCOM, ex-Evalueserve)
Has over 8 years of experience in strategic research across healthcare, life sciences, software products, and start-ups, and has extensively worked with Indian and Global clients, helping in market analysis, digital evangelizing, start-up collaboration, competitive intelligence, and decision making
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There is a huge Vacuum in Indian Healthcare-IT space. Large Healthcare-IT vendors have exited the market. Either they lost interest and exited or got bought out e.g. TrakHealth, iSoft. Also the market is moving from client-server to cloud and from Capex to Opex models. New cloud based players are small in size and yet to reach enterprise class. Existing players are not able to shift out to cloud because of their long term negotiated contracts in client-server model. The time is now when full conversion of Enterprise class to SMAC will happen anyways. Healthcare CIOs can keep eyes closed or tighten the belt and ride the Digital wave.
Recently I spoke to a Director of State NHM in India. He said we are doing HMIS and Public health through ANM/ASHA. How do we benefit from SMAC IoT platform? Hard for many to imagine SMAC is a unifying force across enterprises and IoT breaks the silos. This can be quite unnerving for many.
The era of hierarchical command and control is over. Now is the time for horizontal networking across Communities of Practice [CoP]. Whatever gets the maximum likes becomes the In Thing. Whatever is the In Thing gets used the maximum. Students are learning more from the online networking than from the formal classroom and professors. Research will reach the point of use as soon as it gets published. Primary care Providers in semi-urban and rural areas will have access to latest therapeutic recommendations. The old Adage that ‘Knowledge is the only form of power that is not expendable but grows when shared’ has become true.
The movie Avatar has beautifully depicted the concept ofSmall data ^ = Big Datawhere small knowledge base of each living being [App] is contributing towards the collective consciousness [Big Data] of Eywa. Now the question is will the future of SMAC/IoT be driven by technology or biotechnology?
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SMAC coupled with IoT has a potential to bring the Aggregator Business model to Healthcare. Soon the unorganised and fragmented primary care, secondary care and supporting care market will begin to get Aggregated. I see these Aggregators becoming larger than established capital intensive Enterprise market similar to what happened in the Automobile market. It will be in the interest of Insurance, Pharma and Govt to go all out and support this emerging SMAC/IoT driven Healthcare Market Aggregation.
Digital Health Influencer & SMAC / IoT Speaker | Healthcare Business Executive, Chief Medical Informatics Officer at ProMed Network AG | Managing Partner at TAURUS GLOCAL CONSULTING | Director at Taurus Globalsourcing Inc.