Likelihood of Approval and Phase Transition Success Rate Model – Coronavirus Disease 2019 (COVID-19) vaccine
How likely is it that a drug will get approved? Will the drug transition to the next phase of its clinical pathway? This report provides you with the data to allow you to track and predict specific likelihood of approval (LOA) and phase transition success rate (PTSR) of a drug using a combination of machine learning and proprietary models.
Likelihood of Approval (LoA) – Industry
Coronavirus Disease 2019 (COVID-19) vaccine Drug Details
Coronavirus Disease 2019 (COVID-19) vaccine (CoronaVac) is an inactivated whole virion vaccine composed of inactivated SARS-CoV-2 virus antigen. It is manufactured in a Vero cell manufacturing platform. It is formulated as suspension for intramuscular route of administration. CoronaVac is indicated for the prevention of coronavirus disease 2019 (COVID-19) in individuals aged 3 years and above. coronaVac is indicated for the prevention of Coronavirus Disease 2019 (COVID-19) in patients aged 18 and above adults. CoronaVac is indicated for the prevention of Coronavirus Disease in children 3 years to 5 years of age. Picovacc is under development for the prevention of coronavirus disease 2019 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It is an inactivated vaccine. It is administered through intramuscular route.
The data is segmented by drug name and intervention type and shows the current likelihood of approval for the drug compared to both the indication benchmark and the industry benchmark so you can:
- 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 to generate alpha
Additionally, this data is updated regularly based on events that take place which impact the clinical development process. GlobalData uses its proprietary machine learning models to track event-driven changes in LOA and PTSR and provides the quantitative changes to the likelihood of success along with the qualitative reasoning why the likelihood of approval has changed.
|Quick View – LOA Data|
|Drug Development Status||
Reasons to Buy
- Allows clients to track and predict specific likelihood of approval and phase transition success rates of a drug using a combination of machine learning and proprietary models
- Obtain 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 and industry values
- Use PTSR and LOA information and event driven changes for your investment decisions to generate alpha
Frequently Asked Questions
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.
- 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
Drug Phase Scope:
- Phase I, Phase II, Phase III, and Pre-Registration development stage
Drug Geography Scope:
- 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”
Drug Type Scope:
- Innovator drugs and biosimilars
Entity Type Scope:
Only drugs in development by companies are included in the model.
- 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.
Methodology – Machine Learning
- GlobalData’s Drug-Specific Likelihood of Approval (LoA) models utilizes many attributes from Drugs, Clinical Trials, Review Designations and Company. The proprietary, machine learning algorithm is developed based on the impact of a specific attribute on PTSR and LoA.
|Drug attributes||Trial attributes||Company attributes||Regulatory attributes|
- The attributes with substantial impact on PTSR and LoA have the maximum weightage in the model, while the less impactful attributes have intermediate or low weightage.
- PTSR and LoA scores are dynamic and are modified automatically with dynamic updates in drug, trials and company records. Time dependent variation is largely witnessed in the Clinical Trial attributes and influences the scores in positive or negative direction depending on its implication. This offers the user to examine the real time trajectory of clinical phase transition probability for a drug.
Methodology – Proprietary Model
- GlobalData’s ML algorithm utilizes historical data to determine the probability of successful progression and market authorization for pipeline drugs from their current development stage. The historical data set is prepared to train the ML classifier. Once the algorithm is trained and recognizes a specific pattern, it is applied to the test data. The key learning are further tuned for hyperparameters to derive the optimum model.
ML Model Development
- The ML model is further supplemented with an additional algorithm proprietary to GlobalData to predict PTSR and LoA. Each of the data attributes are assigned a weighted average score based on its direct impact on a drug’s clinical transition. The weighted average score represents a scale between a minimum to a maximum value based on the Raw Scores. The final score of an attribute is achieved by multiplying the Raw Score with the Weighted Score for each data attribute. All scores are added to determine the PTSR value.
- LoA is calculated by compounding the PTSR at each stage the drug is yet to progress through. For example, to calculate the LoA for a drug in Phase I, the following formula is used: PTSR % Phase Ix PTSR % Phase II x PTSR % Phase III x PTSR % Pre-Registration.
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