Somatic Pain is an indication for drug development with over 150 pipeline drugs currently active. According to GlobalData, preregistered drugs for Somatic Pain have a 75% likelihood of approval (LoA) indication benchmark. GlobalData’s report assesses how phase transition success rate (PTSR) and likelihood of approval (LoA) scores for pipeline drugs in Somatic Pain compared to historical benchmarks. Buy the report here.

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Data Insights Likelihood of Approval Analysis for Somatic Pain

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GlobalData tracks drug-specific phase transition and likelihood of approval scores, in addition to indication benchmarks based off 18 years of historical drug development data. Attributes of the drug, company and its clinical trials play a fundamental role in drug-specific PTSR and likelihood of approval.

Somatic Pain overview

Somatic pain refers to a type of pain that originates from the activation of pain receptors (nociceptors) in the skin, muscles, bones, or connective tissues. It’s distinct from visceral pain, which arises from internal organs. Somatic pain is often sharp, localized, and well-defined, resulting from injuries, trauma, musculoskeletal problems, or inflammation. It’s typically easier to pinpoint the exact location of somatic pain compared to other types of pain. Diagnosis involves medical history assessment, physical examinations, and sometimes imaging tests. Treatment approaches include pain-relieving medications like NSAIDs, physical therapy, rest, and addressing the underlying cause, aiming to alleviate discomfort and promote healing in the affected area.

For a complete picture of PTSR and LoA scores for drugs in Somatic Pain, buy the report here.

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GlobalData, the leading provider of industry intelligence, provided the underlying data, research, and analysis used to produce this article. 

GlobalData’s Likelihood of Approval analytics tool dynamically assesses and predicts how likely a drug will move to the next stage in clinical development (PTSR), as well as how likely the drug will be approved (LoA). This is based on a combination of machine learning and a proprietary algorithm to process data points from various databases found on GlobalData’s Pharmaceutical Intelligence Center.