Medical information, including scientific papers, patient data, and clinical trials, has grown over the years; but despite more knowledge, the R&D process in pharma hasn’t really changed. Researchers are struggling to perform routine tasks without unnecessary duplication of efforts.
PhRMA reports that the member companies in the U.S. hit the highest level of R&D investment worth $71.4 billion in the year 2017. Pharmaceutical companies are looking for ways to equip their researchers with smart technologies like AI to assist them and expedite the process. This is the reason why pharmaceutical companies are investing in cognitive technology. Let’s see how artificial intelligence is all geared up to redefine the R&D landscape in pharma.
1. Generates Research Hypothesis
It is estimated that over 2 million research papers are published each year, in about 28,000 journals. Out of which, 50 percent are only read by their authors and editors. Many articles that go unread are buried with information that isn’t reflected in the titles or abstracts. This forbids researchers from having a holistic view of the existing database, which further disrupts the identification of novel linkages between targets and a disease, hindering the generation of accurate research hypotheses.
It is of paramount importance to increase the amount of evidence a researcher can access in order to make informed decisions and create better insights into diseases. It is here that cognitive technology, driven by artificial intelligence, machine learning, natural language processing, text mining, etc., can create a huge difference. It indexes, analyzes, and interprets both structured and unstructured data to surface relevant information more quickly and accurately. Therefore, the accelerated process of identifying novel targets helps to enhance medical innovations by creating original and accurate hypotheses.
2. Enables Computational Molecular Biology & Biochemistry
A significant amount of time during research is invested in gauging a drug’s atomic interactions with its target. By deploying AI and ML-driven technology, researchers can enable computational molecular biology and biochemistry. Cognitive tools are capable of mining genomic, proteomic, and metabolic data from the existing knowledge bases to predict the molecular behavior and the likelihood of discovering or repurposing a drug.
Additionally, cognitive technologies are capable of indexing in vitro and in vivo assays for refinement of computational models of predictive toxicology. With such meticulous screenings, drugmakers can cut out on a significant portion of experiments that were initially planned during Stage I, thus saving ample time and money on unnecessary and repetitive experiments.
3. Facilitates Salt & Polymorph Testing
Determining a drug compound’s level of solubility in water and other liquid is an integral step of the process. This process of screening is known as salt and polymorph testing and helps researchers ascertain the durability of a pill or medicine before it expires. It also helps to choose the best physical form in which a medicine can be manufactured and distributed based on the crystalline structure of molecules.
AI-driven cognitive tools facilitate salt and polymorph screening on multiple levels. The machine learning algorithm helps to discover any existing data related to a drug’s crystalline structure, and predictive analytics analyze this data to give insights into a drug’s structure in the dosage form. Therefore, researchers can better determine the feasibility of a molecular structure in a specific condition without conducting test-tube experiments.
4. Traces a Network of Experts
Research is only as good as the researchers who perform it. Equip the research team with the best of tools and technologies, but lack of researchers’ creativity, skill, flexibility, and sensibility can lead to failure. Therefore, identifying the right people for an R&D project is non-negotiable.
The desired experts for a project are at times scattered across teams or geographies. But fortunately, they leave digital footprints in the form of information they access and create. This is where cognitive search steps in. It analyzes these prints across all touchpoints like trial reports, resource library, etc. to create a dynamically calculated list of veterans that best fit the bill.
By and large, artificial intelligence, machine learning, and analytics don’t just improve outcomes in pharma research and development, but they also optimize clinical trials and accelerate drug time to market.
Looking for a cognitive solution to transform R&D of your pharma company?
Before selecting the best cognitive technology for your business, do keep in mind that researchers need tools that present the data in a comprehensible fashion, annotated with context. Request a free demo of SearchUnify today and find out for yourself if the hunt is over or not. We assure you won’t be disappointed.
