Likelihood of Approval Analysis for Merkel Cell Carcinoma
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Overview
How likely is it that the drugs in Merkel Cell Carcinoma will get approved? Will the drugs transition to the next phase of its clinical pathway? This report provides you with the data that allows you to track and predict the specific likelihood of approval (LoA) and phase transition success rate (PTSR) of drugs using GlobalData’s proprietary machine learning algorithms developed using over 10 years of historical data.

Merkel Cell Carcinoma Overview
Merkel cell carcinoma is a rare aggressive skin cancer that is at high risk of recurring and spreading (metastasizing) throughout the body. Risk factors for Merkel cell carcinoma include age, gender, light skin color, ultraviolet exposure, and immune suppression. Symptoms include painless nodules on the skin. It usually starts on areas of skin exposed to the sun (face, neck, arms, and legs). Treatments include chemotherapy, radiation therapy, and surgery.
Report Coverage
The data is segmented by drug name per indication and shows the current likelihood of approval for industry benchmarks and drug comparisons.
The Likelihood of Approval data is updated regularly based on events that take place which impact the clinical development process and regulatory considerations. GlobalData’s proprietary machine learning models consider these events in real time, to produce quantitative changes to the LOA and PTSR along with qualitative reasoning why the likelihood of approval has changed.
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Reasons to Buy
- Predict the specific likelihood of approval and phase transition success rates of a drug using a combination of machine learning and proprietary models
- Get information on LOA and PTSR for competitors’ drugs to plan your clinical development, commercialization and marketing strategies
- Track event-driven changes in LOA and PTSR benchmarked against indication LOA/PTSR
- Use PTSR and LOA information and event-driven changes for your investment decisions.
This is an on-demand report that will be delivered upon request. The report will be delivered within 2 business days of the purchase, excluding weekends and holidays. Certain sections of the report may be removed or altered based on data availability and relevance.
Frequently asked questions
- Drugs which have been approved in the past 10 years
- Drugs which have failed during clinical development in the past 18 years
- Drugs which are currently in development
- Phase I, Phase II, Phase III, and Pre-Registration development stage
- Drugs must meet one of the following criteria to be included in the model:
- The developer has specified the US as an intended market for approval.
- The developer has not specified any country as an intended market for approval, i.e. the “Drug Geography” is listed as “Global”
- Innovator drugs and biosimilars
- Diagnostics, Imaging Agents, Biomarkers, stents and other drug delivery devices (covered in GlobalData’s Medical Intelligence Center).
- Nutraceuticals, dietary supplements, alternative medicines, imaging agents, radio emitter, transplants, transfusions, fillers, cosmetics, probiotics, antiseptics, antacids, mobilizing agents, veterinary drugs and drugs not seeking approval.
- Generic drugs
- Innovative drugs in Preclinical or Discovery Stage.
- Pipeline drugs sponsored by a Government or Institution.
- Drugs with a specific Drug Geography not the United States.
The probability of a drug ultimately receiving market authorization
The probability of a drug’s advancement to the next stage of clinical development
GlobalData’s Drug-Specific Likelihood of Approval (LoA) calculates the Phase Transition Success Rate (PTSR) and Likelihood of Approval (LoA) customized to individual drug. The model uses a combination of Machine Learning (ML) and a GlobalData proprietary algorithm to process data points from the Drugs, Clinical Trials, Regulatory Milestones, Company, and Financial databases.
Inclusion
Data Scope:
Drug Phase Scope:
Drug Geography Scope:
Drug Type Scope:
Entity Type Scope:
Only drugs in development by companies are included in the model.
Exclusion
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