Artificial intelligence drug design company Iktos, and South Korean clinical research biotech Astrogen announced today a collaboration with the goal of discovering small-molecule pre-clinical drug candidates for a specific, undisclosed, marker of Parkinson’s disease (PD).

Under the terms of the agreement, whose value was not disclosed, Iktos will apply its generative learning algorithms which seek to identify new molecular structures with the potential address the target in PD. Astrogen, which has a focus of the development of therapeutics for “intractable neurological diseases,” will provide in-vitro and in-vivo screening of lead compounds and pre-clinical compounds. While both companies will contribute to the identification of new small-molecule candidates, Astrgoen will lead the drug development process from the pre-clinical stages.

“Our objective is to expedite drug discovery and achieve time and cost efficiencies for our global collaborators by using Iktos’s proprietary AI platform and know-how,” noted Yann Gaston-Mathé, president and CEO of Paris-based Iktos in a press release. “We are confident that together we will be able to identify promising novel chemical matter for the treatment of intractable neurological diseases. Our strategy has always been to tackle challenging problems alongside our collaborators where we can demonstrate value generation for new and on-going drug discovery projects.”

The Second-generation AI systems focus on progress where rather than analysing data for diagnosis assistance, prediction, or tailoring therapy, the second-generation platforms help in improving the biological processes.

BY MEETA RAMNANI

Improving global health requires drugs to be more effective and affordable. While there are multiple branded and generic drugs available, partial or complete loss of response to chronic medications is a major cause that leads to ineffectiveness. Combining this with a lack of adherence by patients leads to even more healthcare issues.  

The first-generation AI systems did not address these needs, which led to a low adoption rate. But the second-generation AI systems are focused on a single subject – improving patients’ clinical outcomes. The digital pills combine a personalised second-generation AI system along with the branded or generic drug and improve the patient response as it increases adherence and overcomes the loss of response to chronic medications. It works on improving the effectiveness of drugs and therefore reducing healthcare costs and increasing end-user adoption.

There are many examples to prove that there is a partial or complete loss of response to chronic medications. Cancer drug resistance is a major obstacle for the treatment of multiple malignancies, one-third of epileptics develop resistance to anti-epileptic drugs; also, a similar percentage of patients with depression develop resistance to anti-depressants. Other than the loss of response to chronic medications, low adherence is also a common problem for many NCDs. A little less than 50% of severely asthmatic patients adhere to inhaled treatments, while 40% of hypertensive patients show non-adherence.

The second-generation systems are aimed at improving outcomes and reducing side effects. To overcome the hurdle of biases induced by big data, these systems implement an n = 1 concept in a personalised therapeutic regimen. This focus of the algorithm improves the clinically meaningful outcome for an individual subject. The personalised closed-loop system used by the second-generation system is designed to improve the end-organ function and overcome tolerance and loss of effectiveness.