Journal of Medical Diagnostic Methods
Open Access

Systematic Medication Indications Development Using Evidence Via Patient Selection Acquired by Computational Informatics

Amin Adham and Waysero Yardley^{* *}Correspondence: Waysero Yardley, Department of Pharmacology, Khurasan University, Jalalabad, Afghanistan, Email:

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Description

If done correctly, indication expansion or drug repositioning can offer a quicker, less expensive and less red flagged path to the approval of new therapies, opening up fresh options to treat patients' specific pockets of unmet medical need and having the potential to have significant positive commercial and clinical effects. We examine the benefits and drawbacks of various repositioning tactics as well as the disease insights and scalability that fresh, high resolution patient stratification approaches can offer. A thorough study of all development candidates and currently available medications demonstrated this by identifying 477 chances for the extension of indications across 30 chronic disease areas, each backed by patient stratification biomarkers. This demonstrates the potential for new AI and combinatorial analytics techniques to increase the rate and cost of innovation across the drug discovery industry.

Despite significant investments in pharmaceutical Research and Development (R and D) in recent years, there hasn't been as much success as anticipated in converting this into breakthrough therapeutic medicines that improve patient outcomes. R and D productivity is beginning to show signs of improvement, although less than 10% of the targets studied in discovery programmers over the past ten years have resulted in medicines that are already on the market. Unfortunately, this has frequently taken the form of costly late-stage phase III failures, primarily as a result of the inability to show clinical benefit in patients. This is largely because many of the chronic illnesses that are so expensive for healthcare systems to treat are poorly understood in terms of the complexities and variations in disease biology across different patient populations.

The creation and analysis of biological and patient databases have seen significant technical advancements over this time. However, our methodology for characterizing disorders and investigating the underlying pathophysiology of complicated diseases has remained mostly straightforward and focused on a single target. Traditional drug discovery methods have focused on a small number of well-studied genes and pathways, even though they are effective in treating relatively monogenic diseases. This has resulted in pools of unmet medical needs, annotation bias, a lack of innovation and even dozens of costly failures within a single mechanism.

Drug development efforts all too frequently focus on targets and disease mechanisms very early on, implicitly presuming that individuals with the same clinical diagnosis have a single common illness cause and that those mechanisms stay relevant and/or treatable throughout the course of their disease. This is oversimplified and fails to adequately represent the biological complexity of chronic illness processes as well as the variety of disease etiologies and effects in various patient subgroups.

Even while patients may ultimately have identical symptoms, it is obvious that diagnoses like schizophrenia, asthma and type 2 diabetes are umbrella words for numerous discrete disease subgroups (or endotypes) that have diverse underlying causes. The prognosis and therapeutic response can vary significantly among patients as a result of this heterogeneity. As a result, a "one size fits all" clinical pathway or "blockbuster" discovery strategy does not work well for complex, chronic diseases, which causes late stage clinical trial failures and frequently requires patients to go through a period of trial and error before receiving the appropriate treatment.

By using patient stratification insights to identify subgroups of patients with similar illness etiologies and thus likely to respond similarly to treatment, new precision medicine approaches can be developed. This creates the patient stratification biomarker tools necessary to speed up and derisk the clinical development of novel targets while also providing a path to innovation.

While this is true, it is also evident that a large number of currently under research and commercially available drugs have potential beneficial effects on pathways and mechanisms that may be common to numerous disease indications. Utilizing such secondary uses within patients whose disease etiology involves these mechanisms can provide a quicker, less expensive and derisked opportunity to bring new medicines to market that address sizable pockets of unmet medical need, to the advantage of both the drug's creator and patients. Such opportunities are being identified at a scale never before possible thanks to new combinatorial analytics techniques based on Artificial Intelligence (AI). Long recognized as a potentially lucrative commercial strategy for pharmaceutical companies, new indication extension (also known as repositioning) opportunities for approved or investigational drugs are found, recently, many cases of repositioning were found by chance, but now that greater biological datasets are available, computer tools are being employed to achieve this systematically.

However, only a few numbers of validated repositioning candidates have been found and success has frequently been constrained by the caliber of the data employed in the research. Even well-known medications that have been repositioned and have strong anti-inflammatory effects, like tocilizumab, which is used to treat patients with severe COVID-19, can fail to provide a clear advantage once they are utilized in the clinic.

Repositioning still faces many of the same difficulties as novel drug research, including developing hypotheses, comprehending the mechanism (s) of action, identifying the patient subgroups in the new indication area that might benefit from the treatment and creating a strong patent position. Additionally, for candidates for repositioning, data on drug safety from databases of adverse event reports and data on toxicity assessment or prediction should be used, along with an assessment of the required dosage and route of administration, to identify any potential safety issues related to the drug and the new indication (s) in question.

Comparing pharmacological properties, such as transcript me or adverse event profiles, with a disease or clinical phenotype is one of the most used drug repositioning techniques like Phenotypic Drug Discovery (PDD). These techniques make use of information from sources like the NCATS open data portal, which has phenotype data from high throughput drug screening tests and the Library of Integrated Network based Cellular Signatures (LINCS), which has been disrupted with a variety of chemical substances. Despite the diversity and volume of these data sources, questions have been raised regarding the accuracy and repeatability of the cellular phenotypic data and it is still difficult to identify drug gable targets. The accuracy, clinical relevance and scalability of such drug repositioning and PDD studies have been impacted by these problems.