Anavex Life Sciences Corp (AVXL)

[XenaLives]

Biomarkers and decision making
BMs have been used to improve patient’s stratification and/or develop targeted therapies facilitating the decision-making process throughout the new drug development process. BMs constitute a rational approach which, at its most optimal, reflects both the biology of the disease and the effectiveness of the drug candidate. Also, adding the appropriate BMs to a drug-development strategy enables the concept of ‘fail fast, fail early’; thus, allowing early identification of the high proportion of compounds that fail during drug development. Reducing human exposure to drugs with low efficacy or safety concerns allows to shift resources to drugs that have a higher chance of success. Identification of BMs helpful for a quick go-no-go decision early in the drug development process is critical for enhancing the probability of success of a drug.

Traditionally, clinical trial end-points, such as morbidity and mortality, often require extended timeframes and may be difficult to evaluate. Imaging-based BMs are providing objective end-points that may be confidently evaluated in a reasonable timeframe. However, imaging techniques are rather expensive and often very impractical especially in specific geographical area.

https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-019-1864-9

In accordance with this, central nervous system (CNS) drugs have lower success rates and take a longer time to develop, than do other drug classes. Specifically, the success rate of neuropsychiatric drug candidates who enter into human testing to effectively reach the marketplace is dramatically lower (8.2%) than for all drugs combined (15%) [5], [6]. In the case of drugs focused toward AD progression, of the numerous evaluated clinically, the attrition rate has thus far been 100%. Furthermore, the average clinical development time for neuropsychiatric drugs is in the order of 8.7 years, as compared with 5.9 years for antiviral agents, almost 50% longer. The time required to gain regulatory approval is also longer for neurological drugs, 1.9 years as opposed to an average of 1.2 years for all drugs. Taking into account the approximately 6 to 10 years that drugs generally are in the preclinical phase of development, neurological drugs can take up to 18 years to run the gauntlet from initial laboratory evaluation to regulatory approval and use [5], [6]—a long duration in relation to the current 20-year patent protection rights. The drug development process is set up, particularly at the stage of clinical development, to “fail fast, fail early” in a strategy to eliminate key risks before making a expensive late-stage investment [7], [8]. Nevertheless, neurological agents tend to fail later during the clinical development process—in phase 3 clinical trials [5], [6], particularly for recent AD experimental therapeutics, thereby making CNS drugs among the most expensive to develop. It is hence important to optimize each piece of the preclinical and clinical development process.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5725284/

Developments in genomics and proteomics have renewed the interest in biomarkers as useful clinical tools. While the speed of development for these technologies has been impressive, the interpretation of data has not yet reached maturity. The time required to characterise and validate new biomarkers often results in them lagging behind other rapidly evolving technologies.

For specific biomarker panels, there is a risk that by the time a biomarker or a panel of biomarkers is validated it may be too late in the drug development process to be of value in decision making. Nevertheless, biomarkers constitute a rational approach, which, at optimal level, reflect the biology of the disease and the effect upon the drug candidate. Appropriate incorporation of biomarkers in drug development strategies can enable the concept of ’fail fast, fail early;’ facilitating the choice of an appropriate critical path toward approval and even differentiation between approved products in a competitive market place. The challenge is therefore to identify relevant biomarkers early enough to implement them for ‘go, no-go’ decisions at critical stages throughout the drug development process (Figure 7).

https://www.europeanpharmaceuticalreview.com/article/1093/personalised-medicine-are-we-ready-for-the-revolution/

"AI is not a magic bullet and is very much a work in progress, yet it holds much promise for the future of healthcare and drug development," says lead author and computer scientist Stefan Harrer, a researcher at IBM Research-Australia.

As part of the review and based on their research, Harrer and colleagues reported that AI can potentially boost the success rate of clinical trials by:

Efficiently measuring biomarkers that reflect the effectiveness of the drug being tested
Identifying and characterizing patient subpopulations best suited for specific drugs. Less than a third of all phase II compounds advance to phase III, and one in three phase III trials fail-not because the drug is ineffective or dangerous, but because the trial lacks enough patients or the right kinds of patients.
Start-ups, large corporations, regulatory bodies, and governments are all exploring and driving the use of AI for improving clinical trial design, Harrer says. "What we see at this point are predominantly early-stage, proof-of-concept, and feasibility pilot studies demonstrating the high potential of numerous AI techniques for improving the performance of clinical trials," Harrer says.
The authors also identify several areas showing the most real-world promise of AI for patients. For example:

AI-enabled systems might allow patients more access to and control over their personal data.
Coaching via AI-based apps could occur before and during trials.
AI could monitor individual patients' adherence to protocols continuously in real time.
AI techniques could help guide patients to trials of which they may not have been aware
In particular, Harrer says, the use of AI in precision-medicine approaches, such as applying technology to advance how efficiently and accurately professionals can diagnose, treat and manage neurological diseases, is promising. "AI can have a profound impact on improving patient monitoring before and during neurological trials," he says.

https://www.sciencedaily.com/releases/2019/07/190717142716.htm