Wednesday, January 30, 2019

AI and Genomic Medicines

 An intermarriage of Biomedical Imaging Technology with Artificial Intelligence.Photo/iStock. 
AI genomic medicine aims to develop qualitative drugs by using deep learning techniques to find related or contrast patterns in genomic and medical data

The next blockbuster drug could be developed with help from machine-learning techniques that are rapidly developing from AI research which aiming to enhance pharmacology labs. The utility of artificial intelligence (AI) has been explored in a multitude of industries (transportation, communication, security) with the healthcare industry being a core focus of AI research in 21st century. Healthcare is a complex industry and AI needed for each component can be varied. The Artificial intelligence involved in healthcare would segregate pharmaceuticals needs from the rigidity perspective of general practitioner, patient, regulatory authority, or health management system. A pharmaceutical based AI projects are on the forefront, most focus in two areas: Patient care platform specifically for diagnostics and therapeutic (drug discovery).


What exactly is artificial intelligence, and how can we use it?


The phrase “artificial intelligence” is unquestionably and undoubtedly, the technology is doing more than ever we knew or realize — whether for both good and bad. It’s already being deployed in health care and warfare; it’s currently assisting people effortlessly make music, write books and conference management; it’s scrutinizing your personal resume, judging your creditworthiness, and tweaking the photos you take on your phone or share on social media. In short, it’s making decisions that affect your life whether you like it or not.  In simplistic terms, AI itself is an algorithm and this can be in the form of software/hardware or application that has the capability to utilize massive amounts of data for multiple tasks. Machines have an advantage over human's brain memory capacity for store, access, and process limitless volumes of information in their memory and apply it quickly to a predefined task (or application).

AI and Drug Discovery: The process of drugs production can take years or even more to come to the market. It cost billions, and can even ruin a company if they fail in late-stage trials having poured in so much investment. The introduction of Artificial Intelligence and the autonomic concept has becomingly more and more important in addressing these issues and this it shows that AI increasingly is the future of drug discovery. The commercial drugs demand faster and better drug discovery as well as delivery.

Artificial Intelligence (AI) in the Pharmaceutical industry and its future innovations.  
Perhaps the most obvious application of artificial intelligence in pharma is using its ability to quickly ‘read’ vast amounts of scientific data: research published in journals, as well as patient records and tissue/blood samples, and using patterns in the data to make scientific hypotheses which can direct pharma companies’ drug development. The speed of AI in these processes allows companies to develop drugs based on biological markers, with greater accuracy, rather than the scatter gun approach of chemical screening. In this way, companies can be zeroing on particular indications which the drug is most likely to successfully treat.
The ever rising costs in drugs research and development, with the frustratingly long time spent in bringing new novel drugs to market and the high rate of failure in the processes needs to be tackled.
Boston-based biotech Berg’s Niven Narain says the company’s AI platform, Interrogative Biology, allows researchers to ping a look into 14 trillion data points in just one single tissue sample. Narain stated that artificial intelligence will halve the time (and potentially the cost). Berg soon will enter its candidate BPM31510 to the market. Similarly, IBM’s Watson supercomputer is currently conducting AI-based trials where it scans mutation data from the tumors of 20 brain cancer patients. This is something that usually would take human scientists several weeks or months to analyze, but Watson can do the same in a matter of minutes.


Through machine learning, Watson gets the process done in better and faster. Ultimately, the screening process could be fast enough to analyze the entire genome of each patient’s individual cancer and for treatments to be tailored based on its specific mutations if they exist. If not, there will be a company interested in putting that right. In the UK, the University of Manchester’s AI platform, known as Eve, can screen more than 10,000 compounds in a day, matching them to likely targets. Again, through machine learning and hypothesis testing, Eve recognizes why ‘she’ has succeeded, and so gets faster the more screening she performs. Lower drug pricing Cheaper drug development should enable cheaper prices. Drug pricing is a hugely controversial issue in the industry nowadays, and the reputations of pharma companies are suffering as a result of massive price hikes. Pharma investors will often justify such increases by citing the huge costs of researching and development, so if such costs can be significantly reduced – as Narain has suggested the cost-effective artificial taking over the pharma – possibly they will no longer be able to use this justification, and prices should (in theory) fall.

 AI can help pharma companies, but right up to approval and even in the general running of the companies. After a promising candidate is discovered, AI could be used to design more effective clinical trials and more quickly analyse the data that emerges from them. Even business decisions may be handed over to supercomputers. Consider the huge numbers of mergers and acquisitions already tendered. AI could more effectively analyse potential synergies gained from the merger of particular companies, allowing them to decide if a combination is worthwhile. If it is, then AIs can help make decisions on integrating the R&D departments, for example. It could also have a hand in the digital sales and marketing process.

In 2015 Eularis released a cloud-based marketing analytics platform for the pharma industry, backed by cutting edge algorithms and the same machine learning capability used in Waymo, which is Google’s driverless cars. These type of AI applications have the abilities to learn the input/output stimuli and effectively mimic them and apply them to new products. The application of AI in pharma is in its infancy, and it could take two decades to reach its full potential. However, the beginnings of a technical revolution that could change the way in which drugs are brought to market appear to have begun already behind the closet and backstage of medical labs, which is good news for pharma companies and patients alike. Where there is data to be analyzed or a business decision to be made, the betting is that the AIs of the future will challenge any current pharma executive to do it better and faster.



The Massachusetts Institute of Technology (MIT) has compiled the “Machine Learning for Pharmaceutical Discovery and Synthesis Consortium.” The group forged collaboration between the pharmaceutical and biotechnology industries and the departments of chemical engineering, chemistry, and computer science at MIT. The goal of the collaborative efforts is to facilitate the design of useful software for the automation of small molecule discovery and synthesis. Pharma companies currently involved in the consortium include are:
  • Amgen
  • BASF 
  • Bayer 
  • Lilly
  • Novartis
  • Pfizer
  • Sunovion
IBM AI research's Watson for Drug Discovery delivers is a cognitive platform and natural language processing trained in the life sciences domain. This AI-based approach facilitates the drugs analysis and processing a massive amount of database more comprehensively and faster than simple search tools or unaided research teams.

Dr. Robert Bowser (Ph.D) the steering hand on IBM Watson Drug Discovery research.  
As according to the Deep Genomics, a Canadian company that uses machine learning to trace potential genetic causes for disease, announced that it’s getting into drug development. It joins a growing list of AI companies betting that their techniques can help produce powerful new drugs by finding subtle signals in huge quantities of genomic data. Deep Genomics was founded by Brendan Frey, a professor at the University of Toronto who specializes in both machine learning and genomic medicine. His company uses deep learning, or very large neural networks, to analyze genomic data. Identifying one or more genes responsible for a disease can help researchers develop a drug that addresses the behavior of the faulty genes. The company will focus, at first, on early-stage development of drugs for Mendelian disorders, inherited diseases that result from a single genetic mutation. These diseases are estimated to affect 350 million people worldwide.

The paradigmatic shift of AI application to the medical world and drug development is partly encouraged by the emergence of some powerful new algorithms, in the market which is cost-effective and new fresh ways of sequencing whole genomes and be able to read out entire DNA genome at once. “There’s an opening of a new era of data-rich, information-based medicine,” Frey says. “There’s a lot of different kinds of data you can obtain today in a brief short period. And the best technology we have for dealing with large amounts of data is none then the machine learning and artificial intelligence.” Deep learning has emerged in recent years as a very powerful way to find abstract patterns using large amounts of training data. It has proved especially valuable for speech recognition and for classification (see “10 Breakthrough Technologies 2013: Deep Learning”). The approach is now rapidly finding new uses in major fields, where it offers a way to spot signs of disease in medical images for predicting disease from a medical record of the patients.

Frey, who trained as a computer scientist and studied at the University of Toronto under Geoffrey Hinton, a key figure in the development of deep learning, says Deep Genomics will seek to partner with a pharma company on drug development. But he adds that the company offers key expertise. “There’s going to be this really massive shake-up of pharmaceuticals,” Frey says. “In five years or so, the pharmaceutical companies that are going to be successful are going to have a culture of using these AI tools.”  The company has published work showing how deep learning can help identify patterns in DNA that might contribute to diseases such as spinal muscular atrophy and nonpolyposis colorectal cancer.

Stephan Sanders, an assistant professor at UCSF School of Medicine in San Francisco who also specializes in using genomics and bioinformatics to study disease, says deep learning could help with drug development by finding patterns in sparse pathology data combined with large genomic data sets. “We have vast amounts of data; three billion data points per individual,” Sanders says. “What we have less of is the other end: clean data of phenotypes or outcomes.”

Several other companies are seeking to apply machine learning to drug development. These include BenevolentAI, a British AI company, and Calico, a subsidiary of Alphabet. Dr. Ken Mulvany, the founder of benevolent, says his company is focused on diseases of inflammation and neurodegeneration and rare cancers. The AI project it aims to tap into largely unused research data. “Developing medicines is still a very lengthy, risky, and expensive process with high rates of attrition,” Mulvany says. “[But] there is an enormous amount of untapped data located in pharma R&D organizations without any plans to develop it.”

Argumentative Assumptions: The world’s leading AI experts and developers stressed that AI should be used for the tedious and monotonous tasks along with human supervisory. Humans are generally well suited with the natural wisdom plus emotional intelligence to “do no harm”, but again our weakness as human beings is that we lack projectable memories and abilities to apply quick response in accessing the volumes of data that may be stored in our brains (subconscious repository) in correlating with a multitude of options or events given. AI has the potential to be ''Yin and Yang'' to a human for the check-balance.

The core differentiators between the capabilities of a human and that of artificial intelligence could be incorporated into a new hybrid model within the pharmaceutical industry whereby AI assists humans to carry out daily tasks with efficiency and expertise. AI may have the “intelligence” for excellence with critical, yet repetitive, tedious tasks such as identification of a new therapy, while human applies “wisdom” required to balance efficacy vs. adverse events.

References
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  13. https://www.forbes.com/sites/forbestechcouncil/2018/05/10/how-data-analytics-and-artificial-intelligence-are-changing-the-pharmaceutical-industry/#3727cbd63644 Artificial Intelligence Already Revolutionizing Pharma
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  16. https://www.analyticsinsight.net/how-artificial-intelligence-is-disrupting-speech-recognition/
  17. https://youtu.be/6tBZA2rygcM 
  18. https://www.linkedin.com/pulse/machines-learning-robots-arent-coming-your-compliance-nathan-lynch/ 
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  20. http://www.pharmexec.com/ai-pharmaceuticals




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