Overall survival: A trusted benchmark, but not always best

The preferred clinical endpoint in oncology clinical trials, OS serves as a trusted benchmark, offering wide ranging advantages over other endpoints^[1][2]. Defined as the time from randomization to death, OS is precise, objective, and relatively easy to measure. Given its clinical robustness and patient relevance, OS is universally accepted by regulators and health technology assessment (HTA) bodies alike^[2][3].

However, in some disease settings, OS is associated with important limitations^[2]. The need for long studies makes OS an unsuitable endpoint in the case of slowly progressing and early-stage cancers, for example. Measurement of OS is also susceptible to confounding, particularly when involving multiple lines of therapies, patient crossover, and the occurrence of non-cancer related deaths. Furthermore, OS does not capture the broader priorities of patients and physicians; for example, when quality of life is a priority over prolonging survival^[2]. For pharmaceutical companies, these limitations can translate into time and financial constraints.

Alternative oncology-relevant endpoints provide opportunities to address these concerns, allowing the collection of data at earlier time points than with OS (Figure 1)^[1]. Such endpoints allow measurement of outcomes before starting subsequent therapies, giving a more direct measure of treatment efficacy. Some endpoints also offer broader value to patients too – besides being surrogate endpoints for OS, non-OS endpoints can provide standalone information, including on symptoms, function, treatment burden, and quality of life^[4].

Figure 1: Alternative oncology-relevant endpoints

Source: Modified from Delgado and Guddati 2021^[1]
Abbreviations: EQ-5D, EuroQol 5-dimensions index; NSCLC-SAQ, Non-Small Cell Lung Cancer Symptom Assessment Questionnaire; PROs, patient reported outcomes.

Why aren’t alternative oncology-relevant endpoints used more widely?

Despite their potential value, alternative oncology-relevant endpoints remain underused in clinical trial design. In 2021, non-OS endpoints accounted for just 16% of primary endpoints in Phase II or Phase III oncology trials, with the most common being pathological complete response, relapse rate and disease-free survival^[5].

Major barriers to the broader adoption of alternative oncology-relevant endpoints in clinical trials include a lack of agreement on their value and the uncertainty among payers that they accurately capture treatment benefits for patients and healthcare systems^[4][6]. While regulators are generally more receptive towards non-OS endpoints, accepting measures that are reasonably likely to predict clinical benefit, HTA bodies typically require validation of surrogacy^[4][6]. In general, guidelines published by HTA bodies indicate a preference for OS data or consider surrogate endpoints only where validation studies demonstrate strong correlation with survival^[6]. From a payer perspective, this caution is arguably expected; several therapies approved on the basis of improvements in outcomes such as progression-free survival (PFS) have not demonstrated OS benefits^[2]. Despite this, few agencies provide detailed methodological guidance for surrogacy validation^[7].

This uncertainty is further confounded by differences in the willingness of national HTA bodies to evaluate even well-established alternative oncology-relevant endpoints such as PFS^[6]. The resulting complexity feeds a vicious cycle: a lack of standardized methodologies for evidence generation leads to insufficient evidence to quantify the long-term benefits of non-OS endpoints, deterring HTA bodies from giving due consideration to these outcomes in decision making^[6].

Moreover, while HTA bodies continue to place particular importance on mortality, patients and clinicians often consider outcomes such as the avoidance of surgery or pain equally or more important than OS in some treatment settings^[6]. HTA bodies and payers can perceive patient reported outcomes (PROs) as more subjective, a point of difference that is reflected in the regional variations in the way PRO evidence is reviewed and considered in global HTA decision-making^[6]. This lack of alignment contributes to a lack of clarity around PRO requirements, reinforcing uncertainty among stakeholders.

Towards stakeholder alignment on alternative oncology-relevant endpoints

Overcoming the uncertainties and inconsistencies limiting broader adoption of alternative oncology-relevant endpoints requires concerted efforts from all stakeholders and a more harmonized approach towards their use^[4][6]. There is growing consensus that, by working together, the oncology community can move closer towards establishing sets of appropriate endpoints for specific cancer types and stages that have wide buy-in from regulatory authorities, reimbursement bodies, pharmaceutical developers, and patients themselves.

A key step towards this goal will be agreeing upon the endpoints that matter most to patients, which will almost certainly differ by cancer type and stage^[4][6]. For example, while extending survival may remain a priority in treatment settings with poor prognoses, for cancers where prognosis is improving, disease progression and quality of life may prove more valuable. Once alignment is reached on which outcomes are most valuable to patients, appropriate endpoints and PROs should then be defined through consultation with clinicians and healthcare professionals^[6].

There also exists an opportunity for greater harmonization in the methodologies used to validate endpoints and generate data^[4][6]. This is particularly important for PROs where there is lack of standardization in data collection methods, analysis and interpretation. Alignment on the levels of uncertainty that are acceptable to regulatory and HTA decision-makers and other stakeholders is also important, requiring greater transparency on the evidence needed to support regulatory approval and reimbursement in specific treatment settings^[4][6]. Much work has already been undertaken to establish the surrogate and standalone value of alternative oncology-relevant endpoints. However, the use of real-world evidence and other health economics and outcomes research (HEOR) studies to evaluate the long-term clinical and economic impact of treatments may help accelerate efforts to bridge these gaps^[6].

Global HTA bodies and professional organizations are already taking steps towards a more harmonized approach to assessing the surrogate and standalone value of non-OS endpoints. For example, an ongoing collaboration between HTA bodies including the National Institute for Health and Care Excellence (NICE), Scottish Medicines Consortium (SMC) and Canadian Agency for Drugs and Technologies in Health (CADTH) is developing methodological guidance and a new joint scientific advice procedure on the use of surrogate outcomes for cost-effectiveness analysis^[8]. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) has also established a taskforce to set good practices for surrogacy evaluation and validation of the relationships between outcomes informing HTA decisions^[9]. These initiatives run alongside broader efforts to harmonize HTA decision-making processes within the European Union under Joint Clinical Assessment, providing an opportunity to promote greater standardization around non-OS endpoints^[10][11].

By supporting more transparent and consistent approaches for the evaluation of alternative oncology-relevant endpoints, these programs have the potential to promote more predictable outcomes in HTA decision-making – a necessary step towards building confidence in the value and suitability of alternative endpoints in clinical trial design.

Looking beyond overall survival

Overall survival remains an important measure of the value of cancer therapies, yet there exists a growing role for oncology-relevant endpoints beyond this ‘gold standard’. By considering patients’ needs, addressing uncertainties, and building consensus around the best outcomes to use in specific treatment settings, oncology-relevant endpoints beyond OS have the potential to facilitate faster and more cost-efficient access to novel cancer treatments.

Clarivate has supported our clients bring the next generation of innovative oncology treatments to market, empowering early-stage R&D and robust clinical trial design, through to navigating regulatory and market access pathways. Within Clarivate’s Evidence, Value and Access consultancy, our reimbursement dossiers, value stories, and objection handlers have supported clients achieve their market access goals in breast cancer, bladder cancer, leukemia and beyond, and our team have expertise with novel technologies such as CAR-T therapies. To learn more about our capabilities and how we can support you, please get in touch here.

This post was written by Richard Massey, Director, Value Communication and Clara Ricci, Senior Medical Writer.

References

^[1]Delgado, A. and A.K. Guddati, Clinical endpoints in oncology – a primer. Am J Cancer Res, 2021. 11(4): p. 1121-1131.

^[2]Cimen, A., et al., Shifting perspectives on the value of non-OS endpoints and PROs: Considerations across stakeholder groups to support oncology HTA decision-making. Journal of Clinical Oncology, 2023. 41(16_suppl): p. e13646-e13646.

^[3]McKee, A.E., et al., The role of the U.S. Food and Drug Administration review process: clinical trial endpoints in oncology. Oncologist, 2010. 15 Suppl 1: p. 13-8.

^[4]Fameli, A., et al., Looking Beyond Survival Data: How Should We Assess Innovation in Oncology Reimbursement Decision Making. Values & Outcomes Spotlight, 2023. 9(5): p. S5.

^[5]IQVIA report, Evolving oncology endpoints – a new horizon for health outcomes 2021.

^[6]European Federation of Pharmaceutical Industries and Federations report, Improving the understanding, acceptance and use of oncology–relevant endpoints in HTA body / payer decision-making 2023

^[7]Grigore, B., et al., Surrogate Endpoints in Health Technology Assessment: An International Review of Methodological Guidelines. PharmacoEconomics, 2020. 38(10): p. 1055-1070.

^[8]NICE, International health technology assessment collaboration expands

^[9]ISPOR, Surrogate Endpoint Statistical Evaluation for HTA Decision Making

^[10]European Commission, Questions and Answers: Adoption of Regulation on Health Technology Assessment

^[11]European Commission, Health Technology Assessment

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In this era of real-world data (RWD) and precision medicine, the quest to unlock the mysteries of rare diseases and tailor treatments to individual patients has never been more promising. Data from a rising tide of sources can empower pharma executive decision making from R&D, clinical studies, health outcomes, drug safety, and market access.

With the wealth of data generated from diverse sources comes the challenge of extracting meaningful insights from the pool of available databases and translating them into actionable strategies. Where the amount of unstructured data is enough to drown you, think of machine learning as your personal flotation device that, when used correctly, can pull you safely to shore.

This begs the question: What computational and analytical tools are available for mining and interpreting rare disease data, and how can companies bridge the gap between data generation and actionable insights?

This subject is discussed in a forthcoming white paper from Clarivate. Hemanth Nair, Director of Real World Evidence Engagement and Innovation at Clarivate, discusses how to apply machine learning to RWD to make foundational decisions on the course of clinical trials, site recruitment and patient identification, while giving real life examples from a recent project with a rare disease client.

From data deluge to actionable insights

Computational tools, databases and platforms are increasingly being used for the enhancement of rare disease therapies, including data mining, data integration, data standardization and quality assurance, as well as visualization and interpretation. With this in mind, what is available, and why should you consider it as part of your rare disease product development?

• Data mining

Machine learning algorithms, including supervised and unsupervised learning methods, can uncover patterns, correlations, and associations within complex datasets. AI-powered approaches such as deep learning, natural language processing (NLP), and neural networks enable advanced data analysis and predictive modeling, offering insights into disease mechanisms, patient outcomes, and treatment responses.

Bioinformatics tools facilitate the analysis of genomic data, including DNA sequencing, gene expression profiling, and variant annotation.

Genomics databases and resources ̶ such as NCBI’s GenBank, the DNA DataBank of Japan, and the European Nucleotide Archive ̶ provide access to annotated genomes, genetic variants, and functional annotations for rare disease research.

• Data integration

Data integration platforms enable the aggregation and harmonization of heterogeneous datasets from disparate sources, including electronic health records (EHRs), patient registries, and omics data.

These platforms facilitate comprehensive data analysis and enable researchers to correlate clinical, genetic, and molecular information, offering a holistic view of disease biology and patient phenotypes.

There are several platforms and solutions available in the market catering to data integration needs in the pharmaceutical industry, each with its own unique features and capabilities. It’s essential to evaluate these platforms based on specific requirements and use cases to determine the best fit for a particular organization’s needs.

• Data standardization and quality assurance

Establishing standardized protocols for data collection, curation, and validation ensures data integrity and interoperability across different platforms and institutions. The Clinical Data Interchange Standards Consortium (CDISC), for example, develops and promotes global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.

CDISC standards cover various aspects of clinical research data, including study design, data collection, data representation, and data exchange. It has standards like SDTM (Study Data Tabulation Model) for organizing and standardizing clinical trial data, ADaM (Analysis Data Model) for analysis datasets, and CDASH (Clinical Data Acquisition Standards Harmonization) for standardizing case report forms (CRFs) and data collection.

Implementing robust quality assurance processes mitigates the risk of errors and inconsistencies, enhancing the reliability and trustworthiness of data-driven insights. Tools are available that have been specifically designed for regulated industries and helps companies to maintain product quality, adhere to regulatory requirements, and improve overall operational efficiency through standardized and automated quality processes.

• Data visualization and interpretation tools

Data visualization tools such as interactive dashboards and heatmaps facilitate the exploration and interpretation of complex datasets, enabling stakeholders to identify trends, outliers, and actionable insights. Advanced analytics platforms offer customizable analytics pipelines and visualization options, empowering users to extract meaningful insights from raw data and communicate findings effectively.

Tools such as RapidMiner, SAS Visual Analytics, Tableau, and TIBCO Spotfire are all popular among the biopharmaceutical and medical device industries to visualize, analyze, and interpret data from various sources.

For more on how Clarivate uses AI and machine learning to help companies develop innovative medicines, devices and diagnostics, generate knowledge and safeguard intellectual property, please visit us here.

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• New drugs developed using new modalities and technologies. Among these:
  • Datopotamab deruxtecan, from AstraZeneca and Daiichi Sankyo, is emblematic of the promise of antibody drug conjugates, an emerging class of highly-targeted anticancer drugs offering hope of an alternative to grueling traditional chemotherapy regimens – in this case, for several types of breast cancer and non-small cell lung cancer. Clarivate data indicates a 90% probability that datopotamab deruxtecan will win marketing authorization in the United States, and projects 2029 sales of $2.7 billion for NSCLC and breast cancer combined.
  • CASGEVY/exagamglogene autotemcel from CRISPR Therapeutics and Vertex Pharmaceuticals and LYFGENIA/lovotibeglogene autotemcel from Bluebird Bio, are two novel therapies for sickle cell disease and beta thalassemia, inherited blood disorders for which there were previously only limited symptomatic and no curative treatments, developed using CRISPR gene-editing technologies. Clarivate projects $1.32 billion in 2029 for CASGEVY alone.
• Treatments for previously-untreatable or undertreated diseases are another highlight. In addition to CASGEVY and LYFGENIA for sickle cell disease and beta thalassemia, these include:
  • ABRYSVO/RSVPreF from Pfizer Inc and AREXVY/RSVPreF3 from GSK plc, the first vaccines for respiratory syncytial virus (RSV) targeted to infants and older adults, a critical advancement in patient care for these most-vulnerable groups as public health officials and clinicians confront the “triple-demic” of COVID-19, seasonal influenza and RSV infection.
  • TALVEY/talquetamab from Johnson & Johnson Innovative Medicine, a first-in-class bispecific antibody for relapsed or refractory multiple myeloma patients, a difficult-to-treat group for whom most if not all treatment needs have been exhausted. Granted U.S. FDA accelerated approval in August, Clarivate projects the drug will see $850 million in sales by 2029.
• New therapeutics that may significantly reduce the burden existing treatments place on patients and clinicians. These include:
  • EYLEA® HD/aflibercept from Bayer and Regeneron Pharmaceuticals Inc, which would offer more convenient 12- and 16-week dosing for patients with wet age-related macular degeneration, diabetic macular edema and diabetic retinopathy. Clarivate Cortellis data indicate a 75% probability of success for diabetic retinopathy in the U.S. and predict $1.77 billion in revenues for the G7 markets by 2029.
  • ALTUVIIIO/efanesoctocog alfa from Sanofi (Bioverativ Therapeutics Inc) and Swedish Orphan Biovitrum AB (Sobi®), the first FVIII replacement therapy for hemophilia A patients that needs only be administered once weekly. Clarivate Cortellis data indicate that there is a 95% probability of the drug being approved in the E.U., and project $1.7 billion in 2029 revenues.

In addition, the China-in-Depth team has identified seven Drugs to Watch for the Mainland China marketplace.

Trends to watch

The report also highlights key trends to watch in the coming year, including:

• The enormous impact gene editing and AI/ML are set to have on medicine, which is already becoming apparent in drug pipelines.
• The effects of the U.S. Inflation Reduction Act, which is forcing companies to rethink their approaches to market approvals and access.
• The uneven maturation of the market for biosimilars, which has made strides, particularly in Europe, while hitting some speed bumps in the U.S.
• The potential for real world data and real-world evidence to transform drug R&D, commercialization and post-market monitoring.

“The year 2024 is shaping up to be the best of times and the worst of times for the pharma industry,” said Henry Levy, President, Life Sciences & Healthcare at Clarivate. “New modalities, underpinned by scientific breakthroughs in the past decade, are achieving clinical successes and providing treatments for patients with previously unmet medical needs. Meanwhile, externalities such as government initiatives to contain healthcare costs, the sustained high cost of capital and global geopolitical disputes are putting a brake on investor appetites for the sector.”

Methodology

To identify this year’s Drugs to Watch, Clarivate drew from the expertise of over 160 analysts covering hundreds of diseases, drugs and markets, along with 11 integrated Clarivate data sets that span the R&D and commercialization lifecycle. The analysis revealed this year’s selections which share the molecules of greatest interest for their categories among those that have either just launched (within the calendar year) or that are projected to launch within the coming year, aiming to identify:

• Drugs projected to become blockbusters ($1 billion-plus in annual revenue) within five years of launch, based on Cortellis data.
• Drugs that stand to significantly advance the standard of care for a condition and deliver big benefits to patient health.

To download the full report, please visit: https://clarivate.com/drugs-to-watch/.

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At Clarivate, a dedicated AI/ML team has been delivering innovative AI solutions across the company’s portfolio for over five years. Leveraging billions of proprietary best-in-class, expertly curated data assets and cross-departmental collaborations, our solutions support multiple use cases, from the use of generative AI (GenAI) for natural language queries of life science data to predictive tools identifying the likelihood of success for deals, clinical trials, drug approvals and more.

Interviews with experts across Clarivate surfaced a list of best practices for the use of AI/ML in drug discovery and development. Briefly covered in our Companies to Watch 2023 report, which spotlights seven innovators changing the drug discovery and development paradigm, we will delve deeper into these best practices here.

Data quality is paramount: garbage in, garbage out

The overwhelming task of curating, connecting and gleaning intelligence from many disparate data assets remains out of reach for many life science companies. Although AI promises to streamline this process, the outputs are only as reliable as the incoming data. Poor quality data can result in an incorrectly chosen target, biased information that has limited relevance for many populations or decisions based on outdated information.

Anyone using a data set, whether in-house, in-licensed or as a partnership, has some responsibility for the quality of the incoming data: from establishing good data governance practices, knowing the data provenance and understanding how data are cleaned and harmonized to continuously checking models for bias and performing quality checks, especially as new data come in.

“High-quality data for AI models is key to achieving high-quality insights. At Clarivate, we have rigorous quality control procedures, and every data point is ultimately overseen by a human expert, even when an AI technology has curated or cleaned the data.”

Transparency around AI/ML outputs can shift perceptions around the technology

Many users of AI systems view the technology as a black box. To instill greater confidence in the outputs, AI developers can provide additional context around the decision-making processes. Although it remains nearly impossible to describe the specific calculations and permutations undertaken by a system to reach a decision, the following information is useful for users to judge the results:

• Strengths and limitations of the data sets
• Weighting of the data
• Specific purpose of the model
• Constraints placed on the model
• Assumptions inherent in the model
• Validation processes

For example, Cortellis Drug Timeline and Success Rates enhances the transparency around its predictions using success indicators. Drug Timeline and Success Rates predicts the likelihood and timing of competing drug launches in the United States, Europe and Asia using historical data, statistical modeling and ML-based predictive analytics. Its predictions can be used to validate internal life science company predictions about an asset’s success and determine if assets are being under- or oversold. The predictions also inform merger and acquisition decisions, by providing an unbiased assessment of which drugs are likely to make it to market.

The success indicators comprise 12 groups of the tool’s more than 100 traits that predict both the timeline and success. These traits include whether the mechanism of action is known, the use of biomarkers to select the molecule, the company’s history with successful drug launches, history of clinical trials for that molecule and more. The tool then visualizes which indicators are positively, negatively or neutrally affecting a prediction, providing visibility into how the forecast was obtained.

“We have taken a very deliberate approach to our AI technologies, by designing safety, security and truth at its center. Not only do we use information that we know to be accurate and true but we also provide perfect traceability of that data so our customers are not burdened with verifying that an answer is correct.”

Close collaboration between data scientists, therapeutic area and compliance experts provides the full view needed to develop effective and reliable models

Generalist platforms relying solely on data scientists often lack the granular life science insights needed to take an asset from discovery to market. Combining domain expertise with technical know-how to design AI/ML models and inform algorithm decisions produces meaningful outputs, such as those provided by the new Clarivate GenAI-driven search platform for life sciences.

A large-scale, company-wide knowledge graph established and refined by our dedicated AI/ML team of data scientists, industry experts, therapeutic area experts and clients underpins the search platform. Expert input incorporated during the design phase enables the platform to place complex, natural language questions within the correct context and return appropriate, understandable GenAI-derived summaries. Suitable for drug discovery, preclinical, clinical, regulatory affairs and portfolio strategy teams, the platform draws from a wide range of validated, traceable Clarivate data sets, as well as our people’s expertise and understanding of what matters to our customers, partners and investors.

“At Clarivate, we have uniquely approached AI development with our dedicated team of data scientists and scientific and industry experts regularly meeting over the last six years to collectively develop our AI solutions. This convergence of a deep understanding of the complexities of AI algorithms and statistical modeling with extensive life science knowledge allows us to rapidly implement AI tools that solve real problems for our customers.”

“Real” intelligence fills the artificial intelligence gaps

Rather than completely replacing conventional methods or wisdom, AI/ML tools create new efficiencies and accelerate understanding and decisions. A human-machine partnership takes advantage of the strengths of both. Many of the Clarivate AI-powered solutions, including Cortellis Deals Intelligence, follow a workflow involving human curation of AI-generated data to ensure the highest quality, accurate outputs.

In the image below showing our pipeline for target identification for drug repurposing, the disease-centric approach uses algorithms to extract knowledge from our Cortellis Drug Discovery Intelligence and MetaBase databases, as well as related content across the Cortellis discovery and preclinical tools and key data from publicly available sources. ML processes identify a list of prioritized targets that are reviewed by Clarivate experts for a refined list of recommended targets. Manual mechanism reconstruction by our experts contributes to the final list of prioritized drug target candidates for the indication of interest and the relevant drugs for repositioning opportunities. The AI-human expert team produces a report for each prioritized target that outlines the supporting evidence and details about the drugs that modulate its activity: mechanism of action, pathways in which it is involved, highest clinical trial phase the drug has ever reached and known adverse effects.

Source: Clarivate

“Domain expertise provides the wherewithal to validate the outputs of AI, and I believe this is what gives Clarivate the advantage over more generalist AI companies. Our AI solutions, paired with our robust, extensive data sets, accomplish 80% of the intelligence gathering, leaving our subject matter experts free to add value to the remaining 20%.”

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Read our recent report on seven innovative AI/ML companies to watch here. The top deal makers and the top innovators across pharma, biotech and medtech trust our intelligence to inform their portfolio and investment strategies. Learn more about how Clarivate supports life science companies around the world with the development and commercialization of life-saving treatments using AI-powered solutions across the Clarivate portfolio.

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The global Clarivate clinical outcome assessment team is comprised of health psychologists and outcomes researchers who have extensive methodological and commercial experience. Our deep and broad knowledge of therapy areas such as oncology, immune disorders, respiratory, psychiatry, CNS, autoimmune, infectious diseases, pain, and women’s health enables sponsors to make informed decisions when selecting, developing and validating clinical outcome assessments (COAs) for use in clinical trials.

In this article, we highlight two case studies which address challenges faced by sponsors looking to incorporate COAs into clinical trials for a number of therapy areas. Data and insights gleaned from these projects have most recently been presented at industry conferences and events, including ISPOR.

The purpose of clinical trials is to understand the efficacy and safety of treatments for the patients who need them. To understand treatment efficacy, it is necessary to measure the impact of the treatment on patients’ signs/symptoms, functioning and overall health-related quality of life. A clinical outcome assessment is a measure that describes or reflects how a patient feels, functions or survives.

COAs include patient-reported outcome (PRO) measures, clinician-reported outcome (ClinRO) measures, observer-reported outcome (ObsRO) measures and performance outcome (PerfO) measures. Including fit-for-purpose measures in clinical trials can allow sponsors to generate critical evidence for regulators to evaluate the efficacy and safety of their product, and after approval these data can inform cost-effectiveness analyses for payer decision making.

Qualitative data and quantitative data are required to develop/modify COAs and demonstrate their measurement properties. These data can take several years to generate, requiring methodological expertise, deep disease understanding, substantial input from patients and other key stakeholders (e.g., clinical experts), and converting regulatory guidance into practical application.

The landscape around COAs is fast evolving

The evidence standards that a COA must meet to support key clinical trial endpoints have become increasingly stringent in recent decades, following the introduction of the United States Food and Drug Administration (FDA) Patient Reported Outcomes (PRO) draft guidance in 2006, followed by the full guidance in 2009. More recently, the FDA has developed the Patient Focused Drug Development (PFDD) Guidance Series, which provides sponsors with guidance on how to collect and submit patient experience data in medical product development for regulatory decision making. The PFDD series will eventually take the place of the PRO Guidance for Industry.

Last year alone the Clarivate COA team conducted over 300 interviews with patients, caregivers or healthcare professionals. We developed or explored over 100 COAs and achieved ethical approval in multiple countries including the U.S., U.K., Mainland China and Japan. These incorporated a variety of methodologies including literature reviews, patient preferences, exit interviews and FDA dossier development.

Patient focused drug development in alopecia areata clinical trials

The Clarivate COA team worked with a sponsor to develop COAs for evaluating key clinical trial endpoints in alopecia areata clinical trials.

The team interviewed patients and clinical experts to conceptualize the disease experience and develop content valid PROs and ClinROs, with accompanying photo guides to assess disease-defining signs/symptoms. Quantitative data from clinical trials informed the psychometric performance of the COAs and thresholds for interpreting within-patient meaningful change. All data were collated in FDA COA Evidence Dossiers.

Several years of evidence generation culminated in the FDA’s approval of the first systemic treatment for alopecia areata with data from the COAs developed by the Clarivate team was included in the approved labelling. Most importantly, newly approved treatments can address significant unmet needs for patients living with severe alopecia areata. The work, said lead investigator Brett King, Associate Professor at Yale Dermatology, “helped change the landscape of alopecia areata forever.”

A qualitative interview study into the experiences of fatigue and depression in chronic hepatitis B

Our experts helped the client to conceptualize the experience of chronic hepatitis B (CHB)-associated fatigue and depression amongst individuals living with CHB in the U.S. Patients participated in semi-structured qualitative interviews with COA experts, designed to elicit concepts important to measure in individuals living with CHB. The results led to the expansion and refinement of a previously developed conceptual model to document the multifaceted experiences of fatigue and depression for patients living with CHB. These findings can inform a patient-centred PRO measurement strategy for clinical trials in CHB.

Clarivate experts can streamline your clinical development strategy with fit-for-purpose PROs and other COA instruments to support regulatory, communication, and reimbursement strategies. To learn more about COA, our broader evidence, value and access offerings or view our industry expert profiles, please visit our website.

About the author

Helen Kitchen is the Vice President of Clinical Outcome Assessment at Clarivate. Helen has 15 years’ experience of selecting, developing, and validating COAs, including PROs, for pharmaceutical clinical trials.

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Patient recruitment accounts for 32% of all trial costs and patient drop out averages 18%, according to Deloitte figures. A new Clarivate report: Harnessing the power of RWD in clinical trials, explores how  trial sponsors are thinking more strategically and incorporating real world data into their trials.  

Real World Data

In recent years, the use of real world data (RWD) has emerged as a powerful tool for enhancing clinical site selection. RWD encompasses a wide range of information, including electronic health  records (EHRs), insurance claims data, patient registries, wearable device data, and even social media content.

In addition to clinical data tied to sites and investigators, RWD allows trial sponsors to know which investigators have experience in conducting a clinical trial in their patient population. Importantly, it can help to identify those PIs that are more likely to have access to their specific patient population of interest.

In therapeutic areas where there is competition for trial participants (e.g. oncology), trial productivity is essential. Identifying sites with oncologists and clinical staff that have expertise in treating a specific cancer type is critical. RWD sources such as cancer registries hold a lot of applicable information on the prevalence of specific cancer types, while patient databases and cancer centers’ historical data are valuable sources for understanding the number of eligible patients and their willingness to participate in clinical trials.

In areas such as rare disease, where patients may be few and often unevenly geographically distributed, RWD can help to optimize site feasibility by adding information from EHRs, patient registries, and claims databases to augment transparency around disease prevalence and patient demographics.

Drinking from the firehose

Valuable real world data is abundant but disjointed, being scattered across myriad formats and public and private databases. This poses a huge challenge for effective utilization, especially for small-to-mid-sized companies. Fortunately, numerous entities, including non-profits, consortia and commercial enterprises, are  gathering and centralizing this real-world data, enabling its meaningful application.

These include patient networks such as PatientsLikeMe, the Innovative Medicines Initiative’s EHR4CR project and the Sentinel Initiative, created by the U.S. Food and Drug Administration.

The importance of trusted and robust data sets that can be joined up and used in tandem cannot be overstated. Clarivate’s Cortellis Clinical Trials Intelligence product overlays clinical sites with incidence and prevalence data, allowing trial planners to see epidemiological data in relation to clinical sites.

On top of this, Cortellis Clinical Trials Intelligence has recently incorporated U.S. claims data, meaning that those using this service can analyze site information overlayed with epidemiological data, and from there can access individual site details for a U.S. hospital to see the claims data in the past 12 months. Having access to clinical data with epidemiological and real-world data, all in one offering, can streamline the site selection process.

RWD is transforming the landscape of clinical site selection, offering a data-driven, patient-centric approach to trial planning. By harnessing the power of data, pharmaceutical companies, research institutions, and healthcare organizations can enhance the efficiency, diversity, and relevance of clinical trials, and thereby realize faster drug development, better treatment options, and improved patient outcomes.

The integration of RWD into clinical research is likely to become even more pivotal in shaping the future of medicine. If trial sponsors can access this data in a trusted and layered way, RWD will save dollars and lives, a win-win for all stakeholders in the healthcare ecosystem.

To learn more about the use of RWD to accelerate clinical trials, download the report, Harnessing the power of RWD in clinical trials. You can learn more about the Clarivate Cortellis Clinical Trials Intelligence solution here.

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At Clarivate, we have been a longtime trailblazer in the implementation of AI to enhance our tools and solutions. For example, we are drawing on our connected data lakes in Cortellis and using machine learning to predict clinical trials progression, regulatory approvals and even valuations on M&A candidates.

We’re mindful of some challenges that life science and healthcare organizations must work through before this technology is mature enough for use in critical business decisions that may impact patient health. These include:

• Ensuring quality input data: AI applications can only ever be as good as the data fueling them. At Clarivate, we curate billions of proprietary best-in-class data assets which feed our machine, deep learning and large language models to power our insights, services and workflow solutions. Standardizing disparate data sets and processes represents a major roadblock to effective use of AI generally, including generative AI.
• Vetting sometimes-spotty output: Generative AI’s “hallucination” problem, wherein large language models produce responses that may be syntactically and semantically correct but factually incorrect, is well-documented. Responsible use of AI demands that outputs receive stringent human oversight to identify and eliminate machine-introduced errors. Our customers entrust our products and services to help them improve patient health, and we will not jeopardize that mission.
• Regulatory asymmetry: Laws governing generative AI vary widely across markets and are evolving rapidly as regulators scramble to address this emerging technology. Italy briefly banned ChatGPT before restoring it, and has vowed to review its competitors. Google and Meta have refrained from launching generative AI products (Bard and BlenderBot, respectively) in Europe, moves interpreted in the press as being motivated either by concern for that market’s stringent privacy laws or in protest of them. The sweeping new AI Act approved by the European Parliament in June requires disclosure of content generated by AI, designs that prevent generation of illegal content and publication of summaries of copyrighted training data used. Other countries are weighing drastic actions over data privacy concerns.
• The intersection of AI and IP: Who owns the data? Who owns the models? How can companies ensure that their data doesn’t fall into the hands of competitors through large language models? Here, again, we see differing approaches by regulators internationally – Japan, for example, has declared training data exempt from copyright protections, and Israel’s Ministry of Justice has staked out a similar position. Regulators in other markets are taking a more cautious approach.  To protect the data of customers and our own, we have adopted stringent company-wide guidelines on the use of generative AI applications and tools.

At the same time, there are some obvious potential use cases that could significantly speed up drug development and better ensure that the right medicines reach the right patients, improving outcomes. These include:

• Assisting in molecule design with desired properties using predictive analytics, a hotbed of pharma dealmaking in recent years, including last year’s Sanofi-Exscientia collaboration, which featured a potential value of up to $5.2 billion (read our recent report on biopharma dealmaking to learn more about activity in this space).
• Predicting safety and efficacy by using large language models to identify relevant documents and ways to optimize existing solutions. As an example, Clarivate recently partnered with VeriSIM Life to use its BIOiSIM® platform, which uses AI and machine learning to predict compound safety and efficacy and help inform go-no go decision making, in tandem with Cortellis Drug Discovery Intelligence data. In addition, large language models may inform the use of machine learning to identify safety-relevant documents.
• Production of synthetic real-world data to augment and improve machine learning models, while maintaining patient privacy.
• Accelerating semantic search across medical and scientific literature to enable real-time natural language searches and curation of vast datasets (e.g., policy trackers) across geographic and language barriers. Clarivate is exploring the use of generative AI to augment the content curation process and to allow advanced search functionality across our interconnected data sets. We recently partnered with Nyqyist Data, Inc. to offer our medtech and research center customers access to clinical and regulatory intelligence from over 500,000 devices and three million clinical studies across major markets using the Nyquist Data platform, which uses proprietary AI-based algorithms to reveal insights previously hidden in unstructured data.
• Making processes more efficient throughout life science organizations and driving costs out of repetitive activities that can be accelerated exponentially.

Jonathan Gear, Chief Executive of Clarivate said: “AI and machine learning are poised to revolutionize how life sciences companies deliver treatments that can transform patient lives. Clarivate was an early adopter of AI technology that enables researchers to optimize treatment development from early-stage drug discovery through commercialization. We are committed to investing in innovative technologies that will support our customers efforts to solve healthcare’s biggest challenges across the entire drug, device and medical technology lifecycle.”

For more information on Clarivate and artificial intelligence, read our announcement regarding the launch of our new artificial intelligence tools.

Podcast: How science and big data are helping in the search for an effective treatment of a rare disease that causes blindness. This podcast demonstrates how AI can make a positive difference in the lives of individual patients.

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Artificial Intelligence at Clarivate

At Clarivate, our dedicated Data Science team have implemented AI across our portfolio to enhance our tools and solutions for decades, and they are forward thinking in their approach to utilizing new technology as it emerges. By bringing clarity to the complex, we give our customers the confidence to make critical decisions, navigate roadblocks and achieve their potential. ​

Artificial intelligence tools are just one of the many ways that we augment our products, tools and services. We use generative AI to ensure we provide our customers with the highest quality integrated public and proprietary content and insights, whilst also mitigating any risks involved.

Of course, artificial intelligence can only ever be as good as the input data. At Clarivate we are well-placed to capitalize on this because we have billions of proprietary best-in-class data assets, which are expertly curated and interconnected. These data feed our machine, deep learning and large language models to enrich our information and power our insights, services and workflow solutions.

Artificial Intelligence for our customers

Academia & Government: For example, we use AI to identify academic journals within the Web of Science that are outliers, to ensure our customers are confident that they can trust all indexed journals and continue to deliver gold-standard content. Trained machine learning algorithms flag these journals to our in-house editors who conduct a thorough analysis of the journal and whether it meets our standards.

Intellectual Property: Another example is the CompuMark Naming Tool which uses generative AI to automate the process of brainstorming new brand names, simultaneously analyzing them for potential usability. Trained on US, EU and Pharma In-Use databases, our proprietary AI algorithm generates creative name suggestions based on your criteria in seconds, simultaneously analyzing them against existing domain names and social media handles.

Life Sciences & Healthcare: A further example is how we draw upon our connected data lakes in Cortellis and use machine learning to generate predictors of future success relating to clinical trials progression, regulatory approvals and even valuations on M&A candidates.

How we protect your data from irresponsible use of AI

We have company-wide guidelines on the use of any generative AI application or tool.  Due to the rapidly-changing nature of the AI landscape, we will constantly review and evolve these guidelines as technology changes.

We are fiercely protective of data from all sources to ensure the privacy and security of our customers and partners. Our internal approvals process ensures checks and balances before using generative AI with any of our products or services, or before allowing any third party to use our content and products within their use of generative AI.

Thinking forward

Our expertly curated data lays a solid trusted foundation, which is built on through the latest tools and our people’s deep expertise in their subjects, as well as their thorough understanding of the risks involved and what truly matters to our customers, partners, and investors. AI is already deeply embedded in our business at Clarivate, and we will continue to look for ways to enhance our solutions and services using the very latest tools.

As a result, we help our customers to think forward no matter what new technologies emerge, knowing they can rely on our partnership.

For more information on Clarivate and artificial intelligence, read our announcement regarding the launch of our new artificial intelligence tools.

Podcast: How science and big data are helping in the search for an effective treatment of a rare disease that causes blindness. This podcast demonstrates how AI can make a positive difference in the lives of individual patients.

The post How Clarivate uses Artificial Intelligence you can trust to transform your world appeared first on Clarivate.

The annual Drugs to Watch report from Clarivate spotlights late-stage pre-launch drugs and biologics set to become blockbusters within five years of market authorization and/or transform treatment paradigms. See who made this year’s list.

We are now seeing personalized medicines representing a significant cohort in new drug approvals. These treatments represent a substantial leap forward for life sciences, in many cases providing treatments for previously untreatable diseases. However, because they are highly targeted, they address smaller potential patient cohorts than the mass-market treatments of yore, forcing pharmas to thread the needle on pricing in order to recoup the costs of development and fund the next cycle of innovation. In some instances, the resulting pricing may prompt payors to place tight constraints on novel therapeutics, limiting both their commercial potential and their potential benefits to patients. Even the most successful new targeted therapeutics now routinely fall short of blockbuster status, which was, in past years, the principal measure of our Drugs to Watch picks.

An updated methodology

With this evolving dynamic in mind, we have revised our Drugs to Watch methodology to include not just drugs forecast to earn more than $1 billion in annual global revenue five years after launch, but also drugs with critical potential clinical impact – those that answer unmet patient need and advance the standard of care significantly. For this year’s Drugs to Watch report, we drew from the expertise of more than 160 Clarivate analysts covering hundreds of diseases, drugs and markets and tapped 11 integrated data sets that span the R&D and commercialization lifecycle to identify 15 Drugs to Watch, including:

• Bimekizumab (BIMZELX®)
• Capivasertib
• Daprodustat (Duvroq)
• Deucravacitinib (SOTYKTU)
• Foscarbidopa/foslevodopa
• Lecanemab (LEQEMBI) and donanemab
• Lenacapavir (Sunlenca®)
• Mirikizumab
• Pegcetacoplan (EMPAVELI®/ASPAVELI®)
• Ritlecitinib
• Sparsentan
• Teclistamab (TECVAYLI®)
• Teplizumab
• Valoctocogene roxaparvovec (ROCTAVIAN)

Key trends

In addition to the growing cohort of personalized medicines, the report touches on several other trends of importance to the biopharma industry in 2023, namely:

• the rapidly growing marketplace for biopharmas in Mainland China, where regulatory reforms have greatly expanded access to treatments and
• the need to balance development of complex biologics that address diseases of concern in the wealthy world, such as the recent burst of innovation in cancer and autoimmune disorder therapeutics, with development of new medicines for global scourges outlined in the United Nations Sustainable Development Goals, including diseases like tuberculosis, malaria and waterborne illnesses.

The urgency of transformative innovation

R&D productivity remains a challenge for pharma in 2022, with a decline in NMEs approved across the globe. This may reflect a COVID-era pivot by companies toward seeking solutions to the disease, as well as the impact of the pandemic on clinical trials and regulator visits due to travel restrictions, etc. These ephemeral factors aside, R&D is getting more difficult, with more complex medicines and emerging but as-yet experimental platforms in play.

Robust data sets, properly used and harmonized, provide an opportunity to accelerate innovation – but in order to realize their potential, companies must be able to combine data sets from disparate sources and coax actionable insights from them. At Clarivate, we work alongside industry leaders to help them navigate the ocean of data and speed much-needed treatments to market in order to help patients live better, longer lives.

You can find the Drugs to Watch 2023 report, along with a host of other resources, here.

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The purpose of clinical trials is to understand the efficacy and safety of treatments for the patients who need them. To understand treatment efficacy, it is necessary to measure the impact of the treatment on patients’ signs/symptoms, functioning and overall health-related quality of life. A clinical outcome assessment (COA) is a measure that describes or reflects how a patient feels, functions or survives.

COAs include patient-reported outcome (PRO) measures, clinician-reported outcome (ClinRO) measures, observer-reported outcome (ObsRO) measures and performance outcome (PerfO) measures. Including fit-for-purpose measures in clinical trials can allow sponsors to generate critical evidence for regulators to evaluate the efficacy and safety of their product, and after approval these data can inform cost-effectiveness analyses for payer decision making.

The regulatory landscape around COAs is fast evolving

The evidence standards that a COA must meet to support key clinical trial endpoints have become increasingly stringent in recent decades, following the introduction of the United States Food and Drug Administration (FDA) Patient Reported Outcomes (PRO) draft guidance in 2006, followed by the full guidance in 2009. More recently, the FDA has developed the Patient Focused Drug Development (PFDD) Guidance Series, which provides sponsors with guidance on how to collect and submit patient experience data in medical product development for regulatory decision making. The PFDD series will eventually take the place of the PRO Guidance for Industry.

The Clarivate clinical outcome assessment team supports sponsors in selecting, developing and validating COAs for use in clinical trials. Qualitative data and quantitative data are required to develop/modify COAs and demonstrate their measurement properties. These data can take several years to generate, requiring methodological expertise, deep disease understanding, substantial input from patients and other key stakeholders (e.g., clinical experts), and converting regulatory guidance into practical application.

Patient focused drug development in alopecia areata clinical trials

In 2017, the Clarivate COA team began working with a sponsor to develop COAs for evaluating key clinical trial endpoints in alopecia areata clinical trials.

Over the next five years the team interviewed hundreds of patients and multiple clinical experts, seeking to conceptualize the disease experience and develop content valid PROs and ClinROs, with accompanying photo guides to assess disease-defining signs/symptoms. Quantitative data were collected in clinical trials and analysed to understand the psychometric performance of the COAs and identify thresholds for meaningful change from a patient’s perspective. These data were eventually collated in COA Evidence Dossiers, submitted as part of the New Drug Application (NDA).

Several years of evidence generation culminated in the FDA’s approval of the first systemic treatment for alopecia areata. Data from the COA development by the Clarivate team was included in the approved labelling.

Most importantly, newly approved treatments can address significant unmet needs for patients living with severe alopecia areata. These COAs helped to bring the patient voice into clinical trial measurement and improved treatment options for patients. The work, said lead investigator Brett King, Associate Professor at Yale Dermatology, “helped change the landscape of alopecia areata forever.”

To learn more about Clarivate clinical outcome assessment services and other health economics and value offerings, please visit our website. 

About the author

Helen Kitchen is the Vice President of Clinical Outcome Assessment at Clarivate. Helen has 14 years’ experience of selecting, developing, and validating COAs, including PROs, for pharmaceutical clinical trials.

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