Gene Expression Prediction and Drug Repurposing

While new drug discovery is the majority of the focus of the Pharma industry to cure diseases, drug repurposability is also a viable option to cure or contain new and existing diseases.

Drug repurposing is the process of identifying new uses for existing drugs. This can be done by studying the mechanisms of action of existing drugs and looking for ways that they could be used to treat other diseases. Drug repurposing can be a faster and cheaper way to develop new treatments than traditional drug discovery.

An example of drug repurposing is the use of metformin (Glucophage) to treat Alzheimer’s disease. Metformin is a diabetes medication that has been shown to slow the progression of Alzheimer’s disease in clinical trials. Metformin is not yet approved by the FDA for the treatment of Alzheimer’s disease, but it is being studied in clinical trials.

Here are some more examples of drug repurposing:

• Thalidomide, which was originally developed as a sedative, is now used to treat leprosy and multiple myeloma.
• Interferon-alpha, which was originally developed to treat hepatitis C, is now used to treat multiple sclerosis and cancer.
• Lopinavir, which was originally developed as an HIV protease inhibitor, is now used to treat Ebola virus disease.

The process of drug repurposing can be divided into the following steps:

1. Target identification: The first step is to identify a target for the drug. This can be done by studying the underlying biology of the disease and identifying the molecules that are involved in the disease process.
2. Screening: Once a target has been identified, a library of drugs can be screened to see if any of them interact with the target. This can be done in vitro (in a test tube) or in vivo (in an animal model).
3. Preclinical testing: If a drug is found to interact with the target in a favorable way, it will then be tested in preclinical models of the disease. This testing is done to assess the safety and efficacy of the drug.
4. Clinical trials: If a drug succeeds in preclinical testing, it will be tested in clinical trials. Clinical trials are conducted in humans to assess the safety and efficacy of the drug.
5. Regulatory approval: If a drug succeeds in clinical trials, it will be submitted to the regulatory authorities for approval. Once approved, the drug can be marketed and used to treat patients.

Challenges of Drug Repurposing

There are several challenges in repurposing that slow down the potential.

First, it can be difficult to identify new uses for existing drugs. This is because the mechanisms of action of many drugs are not fully understood. Additionally, the safety and efficacy of a drug for a new indication may not be known.

Second, it can be difficult to obtain regulatory approval for new indications for existing drugs. This is because the regulatory authorities may require additional safety and efficacy data for the new indication.

Finally, it can be difficult to compete with new drugs that are developed specifically for a particular indication. This is because new drugs are often more effective and have a better safety profile than repurposed drugs.

Despite these challenges, drug repurposing is a promising approach to drug discovery. It has the potential to accelerate the development of new treatments for a variety of diseases.

Here are some examples of technical challenges in drug repurposing:

• Lack of data: One of the biggest challenges in drug repurposing is the lack of data. For many drugs, there is limited information on their safety and efficacy in humans. This makes it difficult to determine whether a drug is safe and effective for a new indication.
• Intellectual property (IP) issues: IP issues are another challenge in drug repurposing. If a drug is still under patent, it may be difficult to obtain permission to use it for a new indication. This can make it difficult to develop and commercialize a repurposed drug.
• Regulatory hurdles: Drug repurposing can also be challenging from a regulatory standpoint. The regulatory authorities may require additional safety and efficacy data for a repurposed drug before it can be approved for a new indication. This can add time and cost to the development process.

Gene Expression Prediction to the Rescue

Gene expression prediction can help in drug repurposing by identifying genes involved in the disease process and potential targets for drugs.

Gene expression prediction is used in fine-mapping disease biology. Gene expression prediction also helps pharma to understand the pharmacodynamic biomarker for how a dose-dependent drug upregulates or down-regulates certain cis or trans-regulatory elements which control the gene expression. This comes in handy during repurposing.

For example, gene expression prediction was used to identify a new use for the drug sildenafil (Viagra). Sildenafil was originally developed to treat erectile dysfunction, but it was found to be effective in treating pulmonary arterial hypertension (PAH) by relaxing the blood vessels in the lungs. Gene expression prediction was used to identify genes that were upregulated in PAH patients and that were targeted by sildenafil. This led to the development of a new treatment for PAH using sildenafil.

Gene expression prediction is a powerful tool that can be used to identify new uses for existing drugs. It has the potential to accelerate the development of new treatments for a variety of diseases.

Here are some other examples of how gene expression prediction can be used in drug repurposing:

• It can be used to identify genes that are involved in the disease process. This information can be used to identify potential targets for drugs.
• It can be used to identify drugs that are already approved for other indications that may be effective in treating the disease. This information can be used to develop new treatments for the disease.
• It can be used to predict the efficacy of a drug for a particular indication. This information can be used to improve the success rate of clinical trials.

Gene expression prediction is a rapidly evolving field of research. As our understanding of the mechanisms of disease and the biology of the human body improves, we are likely to find even more ways to use gene expression prediction in drug repurposing.

One of the challenges in accurately predicting gene expression is that you have to experimentally validate every combination and titration dose of potential drugs until you find the right combination and do that work. This is a long-drawn trial-and-error process and is quite expensive.

To solve for this, a Silicon Valley startup, Cognit.AI, is building an OpenAI-like platform from the ground up to help accelerate gene expression prediction, gene expression engineering, and also a perturbative oracle to conduct CRISPR-like experiments in-silico.

Market Potential

The drug repurposing market is still relatively small compared to the new drug discovery market. In 2021, the global drug repurposing market was estimated to be worth $25.2 billion, while the global new drug discovery market was worth $130.5 billion.

However, the drug repurposing market is expected to grow at a faster rate than the new drug discovery market in the coming years. This is due to a number of factors, including the increasing cost and complexity of new drug discovery, the growing number of approved drugs with known safety profiles, and the development of new technologies that can be used to identify new uses for existing drugs.

Here are some of the advantages of drug repurposing:

• It is faster and cheaper than new drug discovery.
• It can be used to find new uses for drugs that have already been approved for other indications.
• It can be used to find new uses for drugs that have been discontinued.
• It can be used to find new uses for drugs that are not patent-protected.

Drug repurposing is a rapidly growing field of research. As our understanding of disease mechanisms and the human body's biology improves, we are likely to find even more new uses for existing drugs.

Disclosure: The author is also the founder of Cognit.AI