According to a research study conducted by Tufts Center for the Study of Drug Development and published in the Journal of Health Economics, approximately it costs $2.6 B to bring the medicine from the research lab to the consumer. The cost analysis was performed on the top 10 pharmaceutical companies on 106 drugs that were selected randomly for the research study tested on humans from 1995 to 2007. The ClinicalTrials.gov shows the number of registered studies 310, 879 over time as of July 11, 2019 with the combination of drug, surgical procedure, behavioral, and device study and intervention types. In the United States, the average time to bring the drug from the lab to the medicine cabinet is 12 years. Only 6 in 5,000 drugs even make it to the preclinical testing on the actual humans. The probability of one drug getting to the consumer from the lab to the consumer is one out of 5000. The process involves preclinical testing for approximately 3.5 years on animal targeted testing. The investigational new drug application then begins with Phase I clinical trials (averaging 1 year), Phase II clinical trials (averaging 2 years), Phase III clinical trials (averaging 3 years). While, big data analytics tools can potentially lower the healthcare costs to the $493 B dollars (McKinsey, research study), the most important factor in clinical trials is the human. The drug manufacturer takes the new drug application (NDA that consists 100K pages with big data analysis), post-phase III clinical trials for the approval. The average approval time for NDA is approximately 30 months based on 1992 approval study. If you talk to healthcare industry insiders, Californian Woman, Natalie Joy Harp, MBA, entrepreneur, director who spoke at Faith and Freedom Coalition conference, who is an advanced bone cancer patient who tried barbiturates and opioids just to kill the excruciating pain who got cancer due to a medical error of sterile water mix-up. She was wheelchair-bound and tried multiple rounds of chemotherapy and was not given an opportunity to participate in clinical trials. She was even offered Right to Die policies to stop consumption of food and water voluntarily. The Trickett Wendler, Frank Mongiello, Jordan McLinn, and Matthew Bellina Right to Try Act hasn’t been tried for over 45 years. President Donald J. Trump has signed Right to Try Act into law in May last year by providing access to the terminally ill patients with life threatening conditions or diseases to try clinical trials.
The healthcare industry is now in the state of upheaval, it opens up so many participating opportunities for terminally ill patients into the clinical trials. There is an inherent belief that this will expedite and revolutionize FDA approval process and cure cancer and other diseases rapidly decades before the medicines are released into the market. As with any other disruptive technology machine learning algorithms have the potential power to create new drug compounds and medicines and bring it to the clinical trials so rapidly if a human is willing to participate in the clinical trial, it’s unnecessary to see new medicine as a threat. The FDA (Food and Drug Administration) usually does not provide approval to participate in clinical trials to determine if a product is safe to use on the mission-critical investigational and experimental drugs. McKinsey’s estimate shows that the healthcare industry spends $150 B every year on research and development costs to bring new drugs into the market. ATOM’s (Accelerating Therapies for Opportunities in Medicine) most ambitious project is to apply deep learning algorithms for drug discovery at DOE Labs at Lawrence Livermore National Laboratories trough high-performance computing and scanning millions of big data of molecules and genomics with brain-like biological architecture, DANNA (Dynamic Adaptive Neural Network Arrays). Stanford researchers in the healthcare industry are applying one-shot deep learning algorithm on TensorFlow with DeepChem library for drug discovery and expediting the clinical trials. While big data and machine learning tools can expedite the time and reduce the healthcare cost by building the new drug compounds and medicines and expedite the process to bring the drug to the market quickly, saving a human life in time is the mission-critical priority for the drug manufacturers. The Right to Try Act is prolife and provides Right to Live, thus bringing the medicine from the lab directly to the terminally ill patients right away by providing the approved access to participate in clinical trials and consume the drug instantly. Natalie Harp today is a millennial fighter, a cancer survivor, and a living proof of consuming the investigational drugs who lives healthily.
About the Author
Dr. Ganapathi Pulipaka is a Chief Data Scientist at Accenture for AI strategy, architecture, application development of Machine learning, Deep Learning algorithms with experience in deep learning reinforcement learning algorithms, IoT platforms, Python, R, and TensorFlow, Big Data, IaaS, IoT, Data Science, Blockchain, Apache Hadoop, Apache Kafka, Apache Spark, Apache Storm, Apache Flink, SQL, NoSQL, Mathematics, Data Mining, Statistical Framework, SIEM with SAP Cloud Platform Integration, AWS, Azure, GCP with 9+ Years of AI Research and Development Experience and 20+ years of experience as SAP Technical Development and Integration Lead with 30 project implementations for Fortune 100 companies.