FAQs
Check out some of the most frequently asked questions about the industry and our solutions.
FAQs
Check out some of the most frequently asked questions about the industry and our solutions.
FAQs
Check out some of the most frequently asked questions about the industry and our solutions.
INDUSTRY
The probability of technical and regulatory success, abbreviated as PTRS, also referred to as the probability of success (PoS) or likelihood of approval (LoA), indicates the probability that a drug will move from one clinical phase to the next and achieve the ultimate goal of regulatory approval. Therefore, it provides an assessment of the expected chance of regulatory approval. PTRS assessments can change over time as new data, such as readouts from competitors, clinical trial results, etc., are added to the analysis.
The probability of success in drug development is often used interchangeably with PTRS. Likelihood of approval (LoA) is another term that essentially means the same as PTRS and PoS. In short, it’s a way to describe a drug’s chances of receiving FDA approval.
New product planning in the pharmaceutical industry integrates the commercial viewpoint into R&D pipeline programs. Its role is to increase a product’s probability of commercial success by considering factors beyond regulatory requirements.
Drug development is a very time-consuming process. Timelines vary and range from 10 to 15 years (PhRMA). According to a report from the Biotechnology Innovation Organization (BIO), it takes 10.5 years to go from Phase 1 to regulatory approval.
According to a Deloitte report, developing a new asset costs an average of $2.2B.
According to the PhRMA website, only 12% of new molecular entities that enter clinical trials eventually receive FDA approval. Various facts and figures exist based on the data used for the calculation, the indication, and the development phase being evaluated. For instance, between 2016 and 2020, the average industry success rates for Phase II and Phase III were 29–34% and 70–73%, respectively.
AI is well-suited for crunching large databases and looking for patterns due to its ability to process large volumes of data quickly and to detect patterns, trends and relationships within data, including intricate, high-dimensional and complex ones that are difficult for humans or traditional methods to detect. In addition, AI is scalable and adaptable, capable of handling diverse data formats and delivers consistent and reliable results. This allows AI to sift through the large amounts of data needed for accurate PTRS assessments quickly and efficiently and provide valuable insights that can inform decision-making processes in drug development.
Pharmaceutical companies utilize several different traditional approaches to inform their decision-making. The most frequently used ones are:
- Heuristic – Based on an individual’s experience and knowledge related to the domain
- Historical Benchmark – Based on a non-weighted analysis of prior assets developed in the area of interest
- Statistical Analysis – Based on (weighted) regression analysis of single or multivariate factors
AI solves many of the challenges that plague existing ways of assessing the PTRS of a drug candidate. AI can crunch vast amounts of data and detect patterns that neither humans nor existing traditional statistical approaches can detect. If trained properly, AI is objective and unbiased and performs risk assessments consistently, reliably and in a standardized fashion.
INTELLIGENCIA AI SOLUTIONS
Our suite of solutions includes our SaaS-based tool, Portfolio Optimizer, along with Clinical Development Insights, data as a subscription service and custom insights. Visit our solutions page to learn more.
The Intelligencia AI product suite is a powerful tool for professionals in portfolio planning, business development and licensing, program and product strategy and long-term planning. Our customers are mid-sized to large pharmaceutical companies. Additionally, venture capitalists, hedge fund managers and other investment professionals benefit from better assessing a drug’s chances of success.
We have worked with a highly experienced, interdisciplinary team for several years to develop AI models and build the solid data foundation required to train these models. We update the database daily and complement the solution with value-added features such as AI explainability. It is much easier, faster and cost-effective to access the solution we have built using 10,000s of expert-hours, which has proven reliable and valuable in the hands of existing customers.
We curate and harmonize over 1.5B data points that capture both clinical and biological data such as science-drug biology; indications; regulatory designations; patents/funding; genes, proteins, targets, and gene expression; biological pathways; clinical trial design, setup, execution and outcomes, to name a handful. This is supported by a comprehensive data strategy, automation and technology, and expert data curation to create a centralized data repository. If you have specific questions about our process and the data we capture, let’s talk.
Carnegie Mellon University shared an excellent description of explainable artificial intelligence (XAI): Explainable artificial intelligence (XAI) is a powerful tool for answering critical How? and Why? questions about AI systems and can address rising ethical and legal concerns. As a result, AI researchers have identified XAI as a necessary feature of trustworthy AI, and explainability has experienced a recent surge in attention.
Within Portfolio Optimizer, our Explainable AI feature visually shows the drivers behind the AI-driven PTRS prediction. Therefore, AI explainability helps eliminate the impression of a mysterious AI black box and gives you confidence in our PTRS assessments. Our AI explainability blog covers this more.
New data is ingested daily with weekly and regular core AI algorithm updates.
Yes, this is one of the many ways in which the data behind our software solution can be leveraged and manipulated for your specific needs. We also offer a data-as-a-subscription service (DaaS).
Get in touch with us to start the conversation.
Our PTRS assessments boast an 80% prospective and 91% retrospective accuracy* regarding the predictive accuracy of forecasting asset success (FDA approval). To better understand our methodology, reach out, and we’d be happy to share more.
*Data as of February 2024.
We curate and harmonize over 1.5B data points that capture clinical and biological information. Data we collect include science-drug biology, indications, regulatory designations, patents/funding, genes, proteins, targets, gene expression, biological pathways, clinical trial design, setup, execution and outcomes, to name but a handful. Data collection is supported by a comprehensive data strategy, automation and technology, and expert data curation to create a centralized data repository. We describe our data as AI-ready data. If you have specific questions about our process and the type of data we capture, let’s talk
A prospective assessment involves monitoring the predictions of the model on trials that are still ongoing at the time of training and assessing them once the trials have concluded. Prospective assessments are a safer option because they ensure that only information available at the time of prediction is used thus avoiding introduction of a bias and that the assessment metrics can be verified by an external observer.
The accuracy of an AI model can be measured during the model training process; this is called retrospective assessment. It involves setting aside a subset of data from historical trials and not using them to train the model. In the next step, these unseen data are used to assess how good the model’s predictions are. This method can provide a good estimate of the model’s performance but has some drawbacks, especially related to bias.
We use machine learning (ML) algorithms mainly for our work. ML is a subset of AI that focuses on developing algorithms and statistical models that enable systems to learn from data and improve their performance on specific tasks over time without being explicitly programmed. ML is particularly suited for crunching large amounts of data and finding hidden patterns.
COMPANY
The company was co-founded in 2017 in New York. Read more about our company story and leadership.
Intelligencia AI is headquartered in New York, New York, with offices in Athens, Greece. It has a global employee presence and offers a remote-first work environment.
Intelligencia AI primarily works with mid-large pharmaceutical organizations, contract research organizations (CROs), consultancies, and financial markets such as banks and hedge funds.
Please visit our careers page and check back often, as we’re growing and always looking for new talent.
De-risk clinical development and enhance
decision-making with accurate, AI-driven
probability of success.
De-risk clinical development and enhance decision-making with accurate, AI-driven
probability of success.
