artificial intelligence drugs

Artificial Intelligence for new drugs discovery

Biomedical science innovation based on AI technology is the long-awaited opportunity for achieving higher effectivity in this industry. New drug development through R&D innovation in a shorter time and at a lower cost is the Holy Grail of the biopharmaceutical industry.

Scientific innovation not only involves finding the molecular mechanism of a disease but also the development of new drugs for the cure, palliation or prevention of diseases.

Innovation in the pharmaceutical industry costs over 2.400,00 million euros, according to Farmaindustria. On the other hand, R&D global investment in the pharmaceutical sector accounts for 30.000,00 million, only in Europe and these figures hikes to 142.000,00 euros worldwide.

From this amount, 57% goes to design, development and clinical tests evaluation phases. The remaining 40% goes to basic research, approval processes and pharmacovigilance.

According to data provided by Biopharmaceutical representatives, developing a new medicine takes about 12 to 13 years from its discovery to its clinical use in patients. However, only a few molecules reach the commercialization phase. Many are left behind along the phases of the drug development process.

It´s precisely in drug targeting discovery and designs that AI-based techniques have cut downtime by half and costs by 25% in the production of new drugs.

Currently, the Spanish biopharmaceutical, Sylentis, has implemented a software based on Neural Networks, SVM and Machine Learning to gather, filter and reinterpret experimental data generated by the pharmaceutical industry. This allows them to enhance and develop the drugs thanks to a  software that trains to generate thousands of specific compounds to deal with a disease in a matter of a few days. The pharmaceutical company reduces the expensive and time- consuming task of candidate´s selection from years to only a few days.

AI for personalized drugs.

A survey conducted by Deloitte and MIT Sloan Management Review last June found out that only 20 % of biopharmaceutical companies are digitally mature enough, and the lack of a clear vision, leadership and financing are slowing down companies´ growth.

According to MarketsandMarkets, AI´s demand in the biopharmaceutical industry is expected to grow from US$ 198.3 million in 2018 to US$ 3.88 billion in 2025.

The four projected areas to drive most of the AI market forward in biopharmacy between 2018 and 2025: drugs discovery, precision medicine, diagnostic imaging and medical diagnosis and research. The report says drugs´ discovery reached a larger market share during the survey period.

These areas that go from the target candidate molecules selection to the production of the new drug provides a unique opportunity to speed up drugs development. The potential improvement of the process includes:

  • Process redesign to speed up new molecules discovery time and is based on expert knowledge.
  • Digitalization of repetitive processes automation and generation of new content and data.
  • Advanced analytics incorporating internal and external sources. Here new predictive model would be included.

A key role for AI algorithms is molecules interaction forecasting to find the disease mechanisms. In turn, these mechanisms could help setting new biomarkers to identify, design, validate and optimize new drugs candidate target and identify existing drugs that could be reused for other indications


siRFINDER, algorithms to identify target molecules

Grupo AIA has developed siRFINDER, a Machine Learning based software of siRNA molecules which has been successfully implanted by Sylentis, subsidiary of group Pharmarmar.

This software, co-financed by CDTI, is aimed at strengthening drug development based on RNA interference therapy. This tool developed by different Machine Learning algorithms is designed to generate thousands of specific compounds for disease treatment taking advantage of the ARN Interference technology (ARNi). This biomolecular technology enables silencing genes responsible for producing protein associated to specific diseases. Through this ARN interference technology, it is possible to prevent genes from developing as disease.

In a first phase of the project, the algorithms were performed for the selection of the best candidates in terms of their thermodynamic properties, possible body immune response, possible negative effects on the genes and possible modifications or mutations of the target gene. Consequently, we would know which molecules from all the siRNA molecules are the most effective for specific pathologies.

Reducing the number of molecules for further research and development, the time required is reduced by half, which enables Sylentis to develop innovative drugs in a shorter time and lower cost for the pharmaceutical company.

Among the main benefits for Sylentis is that siRFINDER maintains confidentiality of the targets and flow of information during the whole process, because the algorithms collect, clean and reinterpret the data generated by Sylentis describing the expected metrics the siRNA candidates must meet.

Grupo AIA and Sylentis will continue with the following phase of the siRFINDER Project which consists on developing an autolearning system designed to adapt it to the type of tissue it is addressed to.