In silico clinical trials offer a faster, cost-effective alternative for developing treatments for rare diseases, often overlooked due to low financial returns. A recent study on Mycobacterium avium complex (MAC) lung disease showcased the potential of virtual trials, predicting how a widely available antibiotic would be a better alternative to the current treatment.
In recent years, there has been growing attention to rare diseases. These are a group of conditions that often lack treatments due to limited research, or get neglected due to limited potential for return on investment. Some of these diseases are typically rare occurrences in the population or, many times, predominantly prevalent in low-income and developing countries (such as tuberculosis or malaria). The clinical trial phase, which is crucial for evaluating the safety and efficacy of new drugs, is particularly challenging for these conditions, especially considering the burdensome requirements in terms of time and financial investments, in the face of a failure rate that is close to 90%. Therefore, related treatments are often referred to as orphan drugs or orphan devices.
However, in silico clinical trials offer a promising solution to mitigate some of these issues, as they are proven to be faster and more cost-effective than traditional trials.
A recent example involving a rare disease is the proposed treatment for Mycobacterium avium complex (MAC) lung disease. This condition is caused by non-tuberculous mycobacteria, which are common in soil or water and end up easily inhaled by humans. While they often cause no harm to the general population, they can lead to infections in high-risk groups such as individuals with weakened immune systems, chronic lung diseases, or the elderly. MAC lung disease is an emerging public health concern, with a 25% five-year mortality rate, and it appears to be on the rise, partly due to climate change, which is extending the warmer, wetter climates where these bacteria thrive.
The standard treatment for MAC lung disease is an arduous 18-month regimen of three antibiotics, which seem to receive only a 43% cure rate and are associated with significant side effects, as well. Therefore, there is a clear need for more effective and less burdensome treatments.
In a paper recently published in The Journal of Infectious Diseases, researchers from the company Praedicare announced an in silico trial using mathematical and AI models combined with a hollow fiber wet lab system of Tuberculosis, which has been already qualified as a drug-development tool by European Medicines Agency (EMA). This specific trial involved over 10,000 individual virtual patients to test the effects, dose range, response of bacteria, and shortest treatment duration for a single-drug regimen based on the widely available antibiotic Ceftriaxone. The virtual trial predicted a faster remission in a larger proportion of patients, compared to the standard treatment, and revealed other positive outcomes. Notably, this trial was three times faster than a traditional trial, did not involve any animals, and could also predict the results of a phase 3 clinical trial.
These results show the potential of in silico trials to reduce and de-risk animal and human experiments, which ultimately leads to save time and money during the trial phase.
The in silico trial approach presented here, may further be generalised to develop new antimicrobials, treatments for other diseases, or novel drug combination therapies for non-infectious diseases.
Considering the growing body of examples as the one presented above, regulatory agencies like the European Medicine Agency and the Food and Drug Administration are starting to recognise the potential of such in silico technologies, especially in the case of rare diseases, and their orphan drugs/devices. Implementing silico trials could tremendously speed up the development and approval of new drugs and devices, simultaneously de-risking their clinical trials, finally leading to faster and cheaper treatments.
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