When dealing with the business of drug discovery and development, pharmaceutical companies are always hedging their bets with new technologies. The business of creating new drugs is high risk, expensive and long-term. There are no guarantees. The upside can be tremendous. But most of the time pharmaceutical companies are dealing with setbacks, failures, and disappointments. Due to the high-risk factors, drug companies are willing to try new things to increase their chances of success. Naturally, the latest technologies like artificial intelligence (AI), big data and blockchain are making their way into drug research. These technologies might help drug companies find faster and more cost-effective routes to new drug discovery.
Rising Cost of Drug Discovery
Developing drugs was never an easy task. But it’s getting tougher. According to 2014 Tufts Center for the Study of Drug Development (CSDD), drug development costs have increased 145% since 2003. The total cost of developing a drug that is accepted by the market is estimated at $2.6 billion.
CSDD came up with this figure based on the out-of-pocket cost of $1.4 billion plus the $1.2 billion of foregone investment opportunities for investors during the average 10-plus years it takes to develop a successful drug. When post-approval development costs are accounted for it adds another 0.3 million to the total, bringing it to $2.9 billion.
Only 4.1% of overall R&D lead to a successful drug discovery. The above CSDD costs are taking into account those unsuccessful drug discovery attempts.
There are multiple factors contributing to the rising costs of drug discovery and development:
Longer Clinical Trials
Multiple factors are contributing to longer clinical trials:
Lower Success Rates
The success is also getting affected:
So drugmakers are looking at the latest technologies to improve their efficiencies and find ways to decrease clinical trial times.
Artificial Intelligence (AI)
Modern AI development started in the 1950s. Over the years, AI has gone through multiple stages. Early AI algorithms concentrated on optimization problems. It created systems like STUDENT that could solve algebra problems or ELIZA that could be considered the precursor to today’s chatbots.
The next phase of AI development concentrated on computer vision and natural language processing problems which ultimately led to machine learning algorithms. In 2016, when DeepMind’s AlphaGo was able to defeat the legendary Go player Lee Se-dol, it ushered in a new dawn for AI.
Today AI is seeing the emergence of “contextual normalization” programs that can handle variations in data due to contextual factors. It has practical application in the pharmaceutical industry.
The benefit of contextual normalization is that data collected in drug trials can be conducted at a much faster speed. Companies can run more efficient trials with the same amount of resources.
Here are some practical uses of AI in drug discovery:
Drug companies all over the world have started to use AI to improve their chances of finding the right solution. GlaxoSmithKline’s Baltimore-based AI drug discovery unit used AI to search their company’s database to find 230,000 drug candidates that can target various brain ailments like Alzheimer’s, Parkinson’s, depression and more. Another company MACH is using AI to understand biomedical language and find relationships among drugs, diseases, and body protein.
IBM’s Watson was advertised as the ultimate AI solution for the pharma industry. IBM and Pfizer partnered up in 2016 to look into immune system related issues. But Watson wasn’t sophisticated enough to produce tangible results.
But the enthusiasm is still strong for AI in the large pharma market. Big names like Merck & Co, Johnson & Johnson, and Sanofi are investing heavily in AI. These heavy investments will attract more startups to come into AI development for drug discovery.
Businesses are collecting data about their customers at unprecedented speed. This data is known as big data. Most businesses are still trying to figure out how to use this information to get real results.
Pharmaceutical companies can use the already available data to achieve the following things:
Drug companies are wholeheartedly embracing big data to improve current drug discovery processes. The company twoXAR, a biopharmaceutical, has started to leverage big data principles. Instead of spending billions of dollars and 10-plus-years developing drugs, pharma companies can use twoXAR. The company has already used big data to find drug candidates for rheumatic arthritis.
A San Diego-based company Data4Cure has created a biomedical intelligence cloud where researchers can organize data. It combines AI with big data to create up-to-date analytics for pharma companies.
The blockchain is a digital ledger. The idea of blockchain started as the backbone of the cryptocurrency bitcoin. But soon blockchain gained its own momentum. People are rethinking every aspect of their business to use blockchain to improve their operations.
At the core, blockchain is a distributed database. So it’s a great way to keep track of records.
The drug discovery industry is already looking into blockchain as a potential solution for the following:
At the moment, the pharmaceutical industry is concentrating on blockchain-based supply-chain management. A group of drug companies has announced MediLedger. Drug giants like Pfizer and Genentech are involved in this project. The main goal for MediLedger is to stop the circulation of stolen and counterfeit drugs. It will be used to follow a drug from the manufacturer to the hands of consumers. The blockchain will also open up possibilities for keeping track of clinical trials and protecting intellectual property (IP) rights of drug manufacturers.
SAP and Cryptowerk have partnered to develop SAP Pharma Blockchain POC app for Merck and AmerisourceBergen. The app will be able to track various aspect of the drug manufacturing and delivery process. This app can also help drug researchers with their drug discovery and development.
There are also a lot of generic blockchain companies pursuing the pharmaceutical industry. Companies like CargoChain, Everledger, and Provenance can help drug researchers keep track of information for drug discovery processes.
Drug discovery and development is a risky business. So risk mitigation is an important part of ensuring the survival of the industry. With rising costs of drug discovery, pharma companies are moving fast towards adopting AI, big data, and blockchain. These technologies can mitigate the risks for the drug discovery process, increase profitability, and keep the industry sustainable.