The average cost of bringing a new drug to market is $1.3 billion. As suggested by the studies, the prices include failed trials as well. But what if there is a better alternative that can save all this money to utilize it for other causes and still discover the effective medicine?
Possibilities are always there. At one point, it was hard for people to believe even AI devices like Alexa and Siri. But today, they are an integral part of our lives. AI plus machine learning has various applications. One is still in the queue, that is, healthcare drug discoveries. As per multiple studies, the market for AI for Drug Finding is expected to be US$ 8,419 Million by 2026, which is significantly high.
The traditional R&D methods take at least eleven to fifteen years of effort to find a drug, where cost can go up to $2.6 billion. Furthermore, around nine out of ten drugs fail between the first trials making them expensive and ineffective. Because of many such reasons, the pharmaceutical industries are undergoing massive shifts that include AI and machine learning. How is AI going to lead the drug discovery industry? Let’s take a better look.
Why Include AI in Drug Discovery?
The very motive of any drug discovery research is to find a medicine that acts helpfully on the body and can help prevent or aid a particular disease. The traditional way of curating a medicine has been rife with failures, especially during clinical trials. The estimated probability of clinical success is less than 12%, and only 10% of drugs make it to the market bypassing phase 1 trials.
There are various drugs, among which many are small chemically synthesized molecules specifically there to bind target molecules, mostly protein involved in the virus. The researchers carry out large screens of libraries of molecules to find the suitable molecule capable of becoming a drug. After these complex processes, there are many rounds of tests to find this a promising compound.
This process of finding an effective drug by refining various compounds is time-consuming and expensive. Discovering the right drugs does not only face financial issues. Instead, regulatory problems have become a concern, leading to the significant failure rate in selecting novel drug candidates. The issue of growing drug prices and complex regulation of the approval process are two major reasons causing great trouble to pharmaceutical industries and patients.
Therefore, AI can recognize patterns and insights in an accelerated time frame in such circumstances. Undoubtedly, AI caters to a fast and economical option for the development of a protein base. As per the market research firm Bekryl, AI can save over $70 billion in the drug discovery process by 2028.
The usage of AI is not limited to finding new medicines. It is way more advanced and can be applied in analyzing the existing drugs. It can help determine the side effects and general effects of medications on the body, which can offer potential drug repurposing opportunities.
4 Major Applications of AI
·Lead Identification and Compound Screening
Compound screening identifies the compounds that can further prove promising candidates for drug design. It is also done to find out the side effects caused by the administration of compounds. At the same time, Lead optimization refers to how an ideal drug candidate is designed after a lead compound is refined. In both, the process of selecting drug candidates is done with the help of virtual screening and high throughput screening.
This AI-based Virtual Screening is the compound database conducted by pulling huge quantities from publicly available chemogenomics libraries. It includes zillions of compounds annotated with information regarding their structure. It utilizes Support Vector Machines, Artificial Neural network algorithms, and k-Nearest Neighbors to efficiently figure out the potential lead molecule among millions of compounds. Therefore, it speeds up the early stages of drug development.
Intelligent Image-Activated Cell Sorting devices are also reliably helpful in measuring electrical, optical, and mechanical cell properties. It uses AI-based intricate, deep neural network algorithms to accurately separate various cell types in the sample during the cell sorting step.
One of the most significant points while finding a drug has to be target selection. By applying highly efficient AI techniques, the process of selecting the target can be improved. The target discovery platforms driven by AI can synthesize target-related data from a large volume of complex multi-omics information, giving an improved understanding of target biology.
·Enhancing Preclinical Studies
AI has made a real impact, and there is no doubt that it is redefining the preclinical drug discovery process. The preclinical drug designing process involves testing potential drug targets on animal models. Here using AI can efficiently help run the trials hassle-free, allowing the researchers to forecast if the drug will interact with the animal model successfully.
It is surprising to learn that one mobile application AI platform, compared to traditional modified directly observed therapy, has amplified medicine adherence by 25% in Phase II clinical trials. It will be ideal for developing AI tools for clinical trials to identify diseases amongst patients, recognize gene targets, and forecast the effects of molecule designs.
A clinical trial monitoring known as Risk-Based Monitoring is a technique that provides regulatory needs. The usage of AI here can also enhance the conduct of clinical trials in every phase. Especially in Phase II and III of clinical trials to increase the rate of success, AI can play a significant role in identifying and predicting human-relevant biomarkers of disease to select and recruit specific patient populations.
AI and machine learning techniques have revolutionized various fields, and drug discovery is also expecting promising results. The collaboration of AI in finding drugs is happening at a large scale, and the further coming time will tell its effectiveness among patients and practitioners. We can only say machine learning is fruitful if it makes more novel drugs, reduces costs, and speeds up the drug tailoring process.