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

The post Beyond overall survival: Time to agree on the value of alternative oncology endpoints? appeared first on Clarivate.

Liver cancer and focal liver lesions

Liver cancer ranks as the third-leading cause of cancer-related deaths worldwide.^[5] The dominant form of liver cancer is hepatocellular carcinoma (HCC), accounting for 80% of primary liver cancers.^[6] HCC is a type of FLL; however, FLLs also encompass other malignant lesions, such as intrahepatic cholangiocarcinoma and hepatic metastasis, as well as benign lesions such as hepatic haemangioma and focal nodular hyperplasia. Early detection and accurate characterization of FLLs are crucial for optimal treatment decisions and prognostic predictions.^[7]

FLLs are often found incidentally during imaging for other purposes, such as the assessment of abdominal pain or during surveillance for liver metastases in patients with non-liver cancer.^[8] Three main imaging modalities are used for the detection and characterization of FLLs: conventional ultrasound is often the modality to first detect FLLs, after which contrast-enhanced computed tomography (CECT), contrast-enhanced magnetic resonance imaging (CEMRI), or contrast-enhanced ultrasound (CEUS) are used for FLL characterisation.^[8][9][10]

Key modalities used for liver imaging

Ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI) are non-invasive imaging modalities, and each relies on different underlying physical principles (Figure 1). Ultrasound uses sound waves that pass into the body and bounce back from the tissues inside, like an echo. Different tissue types reflect the sound waves differently and software is used to process these echoes to create images of the body.^[11][12]

CT uses a series of X-rays to build images by acquiring multiple projections of the same location from different orientations. Tomographic reconstruction then generates 3D cross-sectional images of the body. The individual X-ray images quantify the reduction in X-ray intensity as they pass through tissues. Different tissue types absorb different levels of radiation (e.g. bones absorb the most) and the resulting differential generates contrast.^[11][12]

MRI employs strong magnets to create a magnetic field in which protons (mostly hydrogen atoms in water molecules) in the body will preferentially align with the magnetic field. By pulsing a specific radiofrequency through the patient’s body, the protons can be forced to spin out of alignment with the magnetic field. After the radiofrequency is turned off, the protons relax back to their resting alignment and release energy. Different tissue types have different relaxation properties, and the released energy can be measured to create an image.^[11][12]

Figure 1: Non-invasive modalities for liver imaging

Enhancing contrast to enhance categorisation

For all three imaging modalities, contrast agents can be used to improve their diagnostic capability. Contrast agents for CEUS consist of microbubbles containing an inert gas stabilized by a shell, enhancing the echo (and thus signal intensity) from the blood.^[13][14] Contrast agents for CECT typically contain iodine atoms that increase image contrast by absorbing X-rays.^[15] CEMRI often uses gadolinium-based contrast agents that shorten the relaxation times of proton nuclei in the tissues, improving signal intensity from contrast-enhanced tissues.^[16][17]

For characterization of FLLs, particularly in patients at high risk of HCC, a Liver Imaging Reporting and Data System (LI-RADS) has been developed to categorize FLLs from LR-1 (definitely benign) to LR-5 (definitely HCC). LI-RADS algorithms specific to CECT/CEMRI and CEUS are available.^[18] Categorization considers factors such as the lesion size and flow of contrast agent through the liver. Because the liver has a dual blood supply (from the hepatic artery and the portal vein), three distinct phases can be assessed during imaging, in which the injected contrast agents (for any of the modalities) ‘wash in’ and ‘wash out’ of the liver: the arterial phase, portal venous phase, and late phase. Depending on the pattern of contrast enhancement of FLLs compared with the surrounding liver parenchyma, the FLLs can be categorized by CEUS, CECT and/or CEMRI.^{[19][20][21][22]}

Advantages and limitations of modalities for liver imaging

All three contrast-enhanced modalities play a role in charactering FLLs, and each has advantages and limitations. For example, CEUS offers real-time imaging, has wide accessibility, can be performed with the clinician/radiographer at the patient’s bedside and is relatively inexpensive.^[10][23][24] However, CEUS has limited penetration depth, affecting imaging in overweight/obese patients, and only one lesion can be studied at a time. ^[22][23][24] Conversely, CECT and CEMRI can visualize deep structures and enable evaluation of the whole liver. ^{[10][15][23][24]} A limitation of CECT is that it uses ionising radiation, and contrast agents for both CT and MRI have restricted use in patients with renal impairment due to nephrotoxic effects.^[10][23][25] CECT and CEMRI cannot be performed in real-time and imaging is performed at predetermined times following contrast agent administration.^[22] They are also performed in a dedicated room, where the patient is confined to a large scanner on their own and may experience discomfort.^[24]

Depending on multiple factors, one modality may be preferred over the other, or multiple modalities may be used, complementing each other.^[8][10][22] Understanding the benefits and limitations of these techniques is necessary to guide the selection of the most appropriate imaging modalities for specific individuals, and assist in accurate characterization and diagnosis of FLLs to avoid delays in diagnosis and treatment, thereby improving patient outcomes.^[4] Such insights can also help optimize medical workflows, and ultimately reduce healthcare utilisation.^[9][24][26]

The Clarivate Value Communication team, helps life science companies present the benefits of innovative and highly technical medical technologies to market access stakeholders in clear, impactful ways. We specialize in the development of robust and compelling value communication materials, including value story slide decks, global value and reimbursement dossiers, objection handlers and publications. We also have extensive experience in primary payer and KOL research, and in developing interactive client workshops and training.

To learn more about our capabilities and how we can support you, please get in touch at https://clarivate.com/products/life-sciences-and-healthcare-consulting-services/evidence-value-access-consulting/.

This post was written by Ulrike Jahnke, Medical Writer, and Paul Cowling, Medical Writer.

References

^[1]Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023 Aug;79(2):516-537

^[2]World Liver Day website.

^[3]European Association for the Study of the Liver (EASL) press release

^[4]Singh S, Hoque S, Zekry A, Sowmya A. Radiological Diagnosis of Chronic Liver Disease and Hepatocellular Carcinoma: A Review. J Med Syst. 2023 Jul 11;47(1):73

^[5]Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209-249

^[6]Rumgay H, Ferlay J, de Martel C, Georges D, Ibrahim AS, Zheng R, Wei W, Lemmens VEPP, Soerjomataram I. Global, regional and national burden of primary liver cancer by subtype. Eur J Cancer. 2022 Jan;161:108-118

^[7]Marrero JA, Ahn J, Rajender Reddy K; Americal College of Gastroenterology. ACG clinical guideline: the diagnosis and management of focal liver lesions. Am J Gastroenterol. 2014 Sep;109(9):1328-47

^[8]Algarni AA, Alshuhri AH, Alonazi MM, Mourad MM, Bramhall SR. Focal liver lesions found incidentally. World J Hepatol. 2016 Mar 28;8(9):446-51

^[9]O’Brien M, Oliver L, Proctor N, Siakantari M, Cantin P, Griffin CP, Stenberg B. Assessing the impact and resource implications of contrast-enhanced ultrasound on workflow of patients with incidental focal liver lesions on the UK national health service. Acta Radiol Open. 2023 Jun 13;12(6):20584601231183131

^[10]Burrowes DP, Medellin A, Harris AC, Milot L, Lethebe BC, Wilson SR. Characterization of Focal Liver Masses: A Multicenter Comparison of Contrast-Enhanced Ultrasound, Computed Tomography, and Magnetic Resonance Imaging. J Ultrasound Med. 2021 Dec;40(12):2581-2593

^[11]Medical Imaging Signals and Systems. Second Edition. Eds: Prince CL, Links JM. Pearson Education Inc. 2015

^[12]Medical Imaging Systems. An Introductory Guide. Eds: Maier M, Steidl S, Christlein V, Hornegger J, Springer Open 2018

^[13]Frinking P, Segers T, Luan Y, Tranquart F. Three Decades of Ultrasound Contrast Agents: A Review of the Past, Present and Future Improvements. Ultrasound Med Biol. 2020 Apr;46(4):892-908

^[14]Yusefi H, Helfield B. Ultrasound Contrast Imaging: Fundamentals and Emerging Technology. Front Phys. 2022;10:791145

^[15]Owens TC, Anton N, Attia MF. CT and X-ray contrast agents: Current clinical challenges and the future of contrast. Acta Biomater. 2023 Nov;171:19-36

^[16]Lohrke J, Frenzel T, Endrikat J, Alves FC, Grist TM, Law M, et al. 25 Years of Contrast-Enhanced MRI: Developments, Current Challenges and Future Perspectives. Adv Ther. 2016 Jan;33(1):1-28

^[17]Chandra T, Mohan S. Role of Contrast in MR Imaging. Top Magn Reson Imaging. 2016 Aug;25(4):151-6

^[18]Marks RM, Masch WR, Chernyak V. LI-RADS: Past, Present, and Future, From the AJR Special Series on Radiology Reporting and Data Systems. AJR Am J Roentgenol. 2021 Feb;216(2):295-304

^[19]Bargellini I, Battaglia V, Bozzi E, Lauretti DL, Lorenzoni G, Bartolozzi C. Radiological diagnosis of hepatocellular carcinoma. J Hepatocell Carcinoma. 2014;1:137-48

^[20]Dietrich CF, Nolsoe CP, Barr RG, Berzigotti A, Burns PN, Cantisani V, et al. Guidelines and Good Clinical Practice Recommendations for Contrast-Enhanced Ultrasound (CEUS) in the Liver-Update 2020 WFUMB in Cooperation with EFSUMB, AFSUMB, AIUM, and FLAUS. Ultrasound Med Biol. 2020 Oct;46(10):2579-604

^[21]Kim TK, Noh SY, Wilson SR, Kono Y, Piscaglia F, Jang HJ, et al. Contrast-enhanced ultrasound (CEUS) liver imaging reporting and data system (LI-RADS) 2017 – a review of important differences compared to the CT/MRI system. Clin Mol Hepatol. 2017 Dec;23(4):280-9

^[22]Wilson SR, Lyshchik A, Piscaglia F, Cosgrove D, Jang HJ, Sirlin C, et al. CEUS LI-RADS: algorithm, implementation, and key differences from CT/MRI. Abdom Radiol (NY). 2018 Jan;43(1):127-42

^[23]Yang Q, Zheng R, Zhou J, Tang L, Zhang R, Jiang T, et al. On-Site Diagnostic Ability of CEUS/CT/MRI for Hepatocellular Carcinoma (2019-2022): A Multicenter Study. J Ultrasound Med. 2023 Dec;42(12):2825-38

^[24]Islam, S.K.M.S., Nasim, M.A.A., Hossain, I., Ullah, D.M.A., Gupta, D.K.D., Bhuiyan, M.M.H. (2023). Introduction of Medical Imaging Modalities. In: Zheng, B., Andrei, S., Sarker, M.K., Gupta, K.D. (eds) Data Driven Approaches on Medical Imaging. Springer, Cham

^[25]American College of Radiology (ACR). ACR Manual On Contrast Media – 2023

^[26]Thrall JH. Appropriateness and imaging utilization: “computerized provider order entry and decision support”. Acad Radiol. 2014 Sep;21(9):1083-7

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What is JCA and when is it coming?

JCA is a centralized European procedure to evaluate the clinical evidence for a product as part of the HTA process. It was formally enacted on January 12, 2022, with the pilot phase and establishment of guidance and stakeholder network being put into place over the last couple of years.

There are, however, still tenders out by the E.U. commission for provision of training of HTABs in how to manage the new process, and there have been a few delays in the overall timing of some elements of the process. JCA becomes mandatory for oncology products and ATMPs on January 12, 2025, for orphan drugs in 2028 and will become mandatory for all drugs going through the European Medicines Agency (EMA) procedure on January 13, 2030.

JCA implementation: key dates

Source: Clarivate

Four key challenges for pharmas in JCA

While the purpose of JCA is to expedite patient access to new treatments by promoting a more efficient and coordinated HTA assessment process, it brings several challenges for manufacturers.

Firstly, the JCA dossier will need to be submitted by companies at around Day 170 of the process, which is before the EMA releases the list of outstanding issues (at Day 180) and before the CHMP opinion (at Day 210). Submitting a dossier without knowing the final population will undoubtedly be challenging for manufacturers as the label (including the indication) will not be final, and may even require the re-start of JCA. In addition, there will only be 90 days between confirmation of the Population, Intervention, Comparator(s), Outcomes (PICOs) and dossier submission, leaving little time to pull together a comprehensive and high-quality evidence dossier without significant preplanning. Furthermore, the submission is likely to happen far in advance of economic discussions and pricing negotiations in individual countries, making it hard to plan strategically for optimal access and reimbursement in each country.

Secondly, the scope of the assessment is a challenge. Even with reassurances in the most recent Implementation Act, with promises of consolidated PICOs as far as possible, it is still likely that for some treatments, there is potential for many PICOs, and this may prove exceedingly difficult for the manufacturer. PICOs will potentially differ where countries consider different relevant comparators because they have different standards of care. They may also define subgroups and surrogate endpoints differently. This is likely to result in significant challenges during the PICO stage of the assessment, especially in determining a clear strategy and narrative and in generating evidence (in the format of meta-analyses, for example) in a timely manner. This is made even more challenging by the apparent lack of manufacturer involvement in this part of the process.

Thirdly, different countries may have different data requirements. Manufacturers will have to address this regardless of JCA. However, we note that JCA does not mean that countries cannot request additional data, and in some situations, it may not be the more efficient process that is promised. Indeed, compiling a dossier for JCA and then responding to requests from several countries (within a 7-to-30-day window) may increase the manufacturer’s workload in some cases.

Finally, we have worked on enough HTA submissions to know that it is not just about putting the clinical and economic data into a dossier and hoping for the best. Successful HTA submissions have a clear narrative running through background, clinical, and economic sections, which is typically the result of months of planning and strategic discussion. While some countries do already have separate clinical and economic dossiers (and even when there is a single dossier, reviewers may only focus on the sections relevant to them), it comes back to the timing -– creating that convincing narrative that runs through all sections of a submission will be made harder by the much earlier submission of the background and clinical data.

And what if it all goes wrong? JCA will not assess economics or make value judgments on behalf of individual countries, and the assessments are non-binding. However, it will amount to a relative effectiveness assessment versus comparators, which may not go in favor of the treatment being assessed. We know from experience that HTA bodies take note of assessments in other countries. Therefore, the impact of an unfavorable pan-European assessment could be huge. JCA is likely to be a challenge for manufacturers. It will impact some products in 2025, and we need to get it right. It is important to understand the benefits of JCA in order to change.

Considering the challenges, timing is going to be key to success with the JCA process.

Gaming out timing is critical to success

While submitting the JCA dossier during the EMA review process will be a challenge, it gives manufacturers an opportunity to optimize some of their processes. For example, it will encourage companies to consider involvement of market access and HEOR teams much earlier in the drug development process –- the HTA process (or at least part of it) will be conducted alongside the regulatory process in Europe, which means that, when planning for evidence generation, both processes need to be considered.

Since July 2017, EUnetHTA and the EMA have offered parallel consultations for manufacturer companies on evidence generation plans -– this is conducted prior to the pivotal trials for a new product, while phase 2 and 3 trials are still at the planning stage. The objective of these parallel consultations, or joint scientific consultations (JSCs), as they are now known, is to help generate optimal and robust evidence to satisfy both regulators and HTA bodies. It should be noted that there is a finite number of JSC slots available and there are specific eligibility criteria for JSC requests, including:

• unmet medical need for the product
• being first in class
• a large potential impact on patients, public health, or healthcare systems
• a significant cross-border dimension
• and major E.U.-wide added value or clinical research priorities

These criteria can make it difficult to secure a slot.

Even if a company does not wish to participate in a JSC, or if a treatment is not eligible for JSC, there is a compelling argument for considering HTA requirements much earlier in the drug development process. For example, with earlier HTA engagement, clinical trials and real-world studies could potentially be designed to incorporate specific comparators, endpoints, or subgroups relevant to HTA that may not have been necessary from a regulatory perspective but may satisfy regulatory needs. Of course, it may make pre-launch evidence generation planning more complex, and sometimes, it may not be feasible to incorporate all endpoints, comparators, or subgroups that may be relevant to HTA, but at the very least, it facilitates informed planning and decision making.

“We have been working with DRG/Clarivate on a high-profile orphan drug in rare diseases. We have engaged them to lead our International HTA Network to ensure that we submitted high quality HTA Dossiers in Europe.​ They have demonstrated strong organizational skills, eye for detail and ability to manage external consultancies and deal with internal stakeholders, assertively and diplomatically. “

Early planning related to the communication of treatment value will also become increasingly important with JCA. While economics will come later in the process, companies will need to develop a strategy and value narrative in preparation for JCA submission that will still be applicable during country-specific economic evaluations and pricing negotiations -– this is likely to involve early economic modelling to prepare for the economic scenarios. It will be important to consider relevant messaging for individual countries, but the narrative running through the JCA dossier must be more general global (or at least pan-European).

JCA, RWD and RWE

On a more granular level, predicting PICOs internally in order to plan for evidence generation activities will be important. The key to success with JCA will be early planning and collaboration. Companies really need to be thinking about how their various teams engage at different points in the drug development process and, particularly for market access and HEOR teams, this will need to be much earlier, when during evidence generation planning for pivotal trials.

Real-world evidence is used increasingly frequently in HTA, enabling a more robust critical assessment of technologies and can validate whether the study population and clinical context of a RCT is reflective of clinical practice. RWE offers enormous potential to inform multiple aspects of HTA, including:

• Epidemiology
• Current management
• Clinical and economic outcomes

However there remain challenges for using RWE in HTA, including skepticism about the validity of evidence and that evidence generation timing may not be synchronized with HTA and pricing bodies’ agendas.

Strong partnership among all stakeholders and pragmatic use of existing data alongside clinical evidence provided by companies are key success factors.

Case study: using RWD to inform a cost-effectiveness model to demonstrate the benefits of an inhaled antibiotic for managing a chronic pulmonary infection.

A large pharma company completed a head-to-head trial comparing their inhalation solution using a nebulizer handset with an existing treatment for a chronic pulmonary infection. In the trial, the new inhalation solution demonstrated both clinical and economic benefits compared with the existing standard of care, showing improved lung function and reduced risk of hospitalization.

The client required a model to communicate the value of the inhalation solution and nebulizer handset to national payers in order to demonstrate the cost effectiveness of their new treatment compared to standard of care for inclusion as part of their HTA submissions.

To meet these requirements, a compelling economic model was needed to evaluate the cost-effectiveness of a new inhalation solution administered via a nebulizer compared with standard of care.

Insights generated using Clarivate’s RWD ecosystem of patient-level records enabled the quantification of key areas of differentiation (improved lung function, reduced HCRU) compared with standard of care.

These insights were used inform a cost-effectiveness model to demonstrate the benefit of reduced drug, hospitalization and lung transplantation costs.

This model supported a national HTA submission, and country adaptations were created to aid the client’s local affiliates. This solution:

• Empowered our partner to demonstrate the clinical and economic value of the new inhalation solution and nebulizer system as an alternative to standard of care, in compelling format that was both robust and commercially and strategically optimal
• Provided a user-friendly and flexible cost-effectiveness analysis with scenario and sensitivity analyses to explore potential scenarios and uncertainty around key input data, and support for country adaptations of the model
• Delivered impactful messaging around the value of the product to key stakeholders

Does RWD/RWE influence HTA decisions and market access?

HTA Bodies acknowledge that RWD/RWE should become an essential component of HTA processes; their availability allows questions left open by the initial submission package to be answered.

But generating good evidence is challenging. It challenges the IT infrastructure of each health system, which in each country has its own history contingent on its welfare model, to its supply of healthcare (public or private/public mix), and to its national governance (centralization vs decentralization).

The burden of providing controlled evidence for a new treatment is borne by companies, while for RWD/RWE, it is shared between them and HTA bodies. This leads to an additional administrative burden. The time and skills needed to provide good evidence should be considered.

If you would like to discuss your 2024 plans with any of our R&D, commercialization or technology enablement teams, please connect with our experts here.

This post was written by Ruth Howells and Oliver Blandy.

Ruth Howells is Head of Health Technology Assessment (HTA) at Clarivate. Ruth is responsible for our Global HTA services and has been involved in the development and strategic direction of HTAs, contributing in-depth knowledge of procedural processes. Full bio here.

Oliver Blandy is a Senior Consultant at Clarivate. Oliver provides epidemiology and real-world data insights and solutions to meet the business needs of decision makers within the pharma and biotech industries, working across multiple therapeutic areas including cancer, infectious disease, and rare diseases.

The post Navigating Joint Clinical Assessment and the role of RWE in the new process appeared first on Clarivate.

For some products, the point of failure is a single oversight or miscalculation. Others stumble across multiple hurdles before finally calling it quits. However, not all product missteps end in outright failure –some companies are able to learn from their mistakes in real time, salvage their development efforts and emerge triumphant.

From each of these scenarios, valuable lessons can be learned and folded into the next development cycle. A review of multiple product setbacks and failures by Clarivate analysts revealed that they stemmed from a few common reasons:

• Lack of early communication and collaboration across departments: Scientific discovery alone is often not sufficient to guarantee market success, yet R&D and early-phase trials continue to operate independent of clinical development, regulatory, commercial and market access teams. Without insight from across the company about what will drive success when the product emerges from the lab, issues occur when it can be too late or cost-prohibitive to implement countermeasures. Early due diligence informs a program design that addresses concerns across the entire development lifecycle.
• Misinterpreting or not anticipating stakeholder expectations. It is not sufficient to assume that what worked during the last product market launch will meet the ever-evolving priorities of patients, physicians, governments, regulators and payers at the next product launch. Furthermore, what is considered appropriate for one country or region might not apply to others. Keeping a finger on the pulse of stakeholder demands helps identify products (molecules, platforms and devices) that answer unmet needs; establish clinical trial designs to collect the data needed to demonstrate efficacy, safety and benefit beyond merely clinical outcomes; and communication strategies to effectively differentiate the product.Even for biotech companies looking to be acquired after promising early-phase results, investors and potential partners are seeking confirmation that the product will be viable once it reaches the market—will it be acceptable to use, considered reimbursable, and does the value outweigh its risks? These stakeholders want to avoid inheriting poor choices that have already become part of the fabric of the product development process.
• Not addressing red flags as they occur. Early regulatory feedback about additional data needed, competing developments in the same drug class/therapeutic area and criticisms of pricing are all examples of pending roadblocks that companies have ignored along the way to submission and market launch. The cost of continuing, in both resources and company reputation, was often higher than if the companies had paused to re-evaluate and adjust their strategies or to make crucial go/no-go decisions earlier.

Reviewing and analyzing past failures and recoveries highlight the types of data that can inform future product and clinical trial design as well as commercial launch, helping future-proof product candidates from early R&D through commercialization. These include real-world data about:

• Patient unmet need
• Biomarkers and disease/patient segmentation
• Clinical trial sites in patient and physician-dense areas
• Safety and efficacy signals of possible concern
• Geographic differences in regulatory requirements
• Evolving government policies and initiatives
• The clinical and competitive landscape surrounding a developmental product
• How to accurately value product candidates to secure funding and inform pricing and market access strategy

Hear from Clarivate analysts about specific examples of products that were overlooked, failed to gain market approval or struggled at launch in our Learning the Lessons of Failure: How to improve the economics of life sciences R&D webinar and how these roadblocks could potentially have been overcome with earlier, better-informed planning and monitoring.

The post Future-proofing drug R&D to ensure a successful commercial launch appeared first on Clarivate.

In the research and development space, we help companies advance their drug discovery, preclinical proof-of-concept, and regulatory activities.  Clarivate’s translational science expertise, regulatory and commercial experts and assets combine to complement client teams in generating long term value.

I’m therefore particularly proud of this newly published case study where Gedeon Richter and Clarivate worked closely to transform regulatory workflow, connect data and improve decision making.

Our client is a major pharmaceutical company headquartered in Budapest, Hungary, with an expanding presence in Western Europe, Mainland China, Latin America and Australia. Its product portfolio covers many important therapeutic areas, including women’s healthcare, central nervous system and cardiovascular health.

53% of pharma CEOs report regulatory changes as a top disruptive trend*

Gedeon Richter had a need to streamline regulatory monitoring and impact assessments. Each day, thousands of regulatory and industry-related documents are released from different regions and companies all over the world. Before partnering with Clarivate, teams would spend hours manually triaging these regulatory documents to identify and communicate impacts across their organization.

This required a significant amount of time to sift through multiple regulatory intelligence sources to identify and locate essential data. Gedeon Richter staff would travel to the sites of EMA (European Medicines Agency), FDA (Federal Drug Administration) and other regulatory authorities to manually review updates in regulations or guidelines. These documents would be downloaded and sent manually via email to subject matter experts.

Gedeon Richter is one of the earliest subscribers to the Clarivate Regulatory Intelligence Tracking Application, an industry leading solution and companion to Cortellis Regulatory Intelligence.  This end-to-end solution helps organizations manage regulatory intelligence by providing a single source of truth, unifying and making that data more actionable.  Our consultants have helped empower professionals at Gedeon Richter to think and act more strategically by providing:

• Customizable dashboards that deliver the regulatory update data specific to regions and topics of interest
• Solutions powered by AI that automates the daily processing of new regulations and legislation
• A source for all departments to complete and view impact assessments.
• A notification and alerting system that keeps their experts up to date on the latest regulatory requirements around the world
• A searchable storage solution that consolidates regulatory updates

The impact of a trusted partnership

Now, with unified data sources from Clarivate, Gedeon Richter has more visibility into regulatory changes and can reinvest time on higher value activities such as actions and impact assessments.

Technology and regulatory consultants from Clarivate provided full support during Gedeon Richter’s transition into the tracking application.

“Clarivate has been very approachable and responsive to taking our business needs into consideration when working with the tracking application. We are now able to efficiently triage and manage our regulatory alerts, send out newsletters with ease, and so much more!”

Our worldwide roster of clients can call upon a 300-strong team of practice leaders, therapy area specialists, data scientists, industry experts and analysts who take the time to understand client needs so that we can co-create transformative solutions like this Gedeon Richter example.

If you would like to discuss your 2024 plans with any of our R&D, commercialization or technology enablement teams, please connect with our experts here.

*Source: https://www.nature.com/articles/d41573-022-00001-9

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R&D productivity fell in 2022, but late-phase cancellations are also trending down

The rise and fall of R&D productivity across the global biopharmaceutical industry is a well-documented and much discussed topic, with industry commentators quantifying productivity in many ways (e.g., forecast peak sales per asset versus the cost of developing an asset or an output measure such as the number of global first world launches).

The common thread in all these different metrics is that R&D productivity is measured as a function of an input – for example, R&D expenditure — versus an output, such as sales of newly launched products. In addition, R&D productivity analyses are in some way associated with actual or future launches or a consequence of the launch, such as resulting sales. So, a simple and obvious surrogate for R&D productivity, which considers an output component only, is the number of New Molecular Entities (NMEs) launched annually.

Figure 1 illustrates the trend in the number of NMEs first launched onto the world market between 2013 and 2022. Looking at NMEs launched globally in 2022 paints a gloomy picture at first sight, with a 28% drop from 2021 to 2022. While some industry observers read this drop as a clear decline in productivity, others take a more optimistic view, attributing the decline to FDA submission calendar fluctuations and noting that 2022 numbers were not very different from the ten-year average. In fact, the number of NMEs has grown by 65% between 2013 and 2022.

Figure 1: Number of NME launches from 2013 to 2022

Analyses from the 2023 R&D Factbook also indicate that the number of late phase terminations has consistently declined in recent years. Together, these trends paint a positive picture for the biopharmaceutical industry, suggesting that the industry is getting better at “doing more with less” and progressing only the more promising compounds, thereby improving productivity.

Spend is growing, but ROI is TBD

However, when looking at the potential commercial value of this R&D activity, some challenges remain.

R&D spend is steadily growing and is expected to reach approximately $200 billion by 2025. This amounts to approximately 20% of sales revenues which are reinvested into developing pipelines. However, only 10% of global biopharmaceutical sales were derived from products launched in the preceding five years, the rest of revenues coming from established products. CMR analysis suggests that this trend is likely to continue in the near term.

The industry is focusing on newer technologies and modalities to drive innovation, including cell and gene therapies, antibody drug conjugates, AI and machine learning, along with novel targets, predictive biomarkers and newer, more complicated study designs. However, their full impact on value remains to be realized.

Altogether, the data highlight an improvement in the volume of R&D productivity, while the value of this innovation remains uncertain.

The 2023 CMR International Pharmaceutical R&D Factbook features metrics and analyses on R&D productivity and many other key topics relevant to the biopharmaceutical industry, such as R&D resources and pipelines, patents and generic drugs. Learn more about purchasing it here.

For over 20 years, CMR International has been a trusted partner to the largest and most innovative pharmaceutical companies to help them assess R&D productivity and provide insights and decisions on industry trends. To learn more about how Clarivate and CMR International help pharmas future-proof their portfolios, please visit us here.

Jasmin Mehta is a Senior Manager within the Life Science Professional Services team at Clarivate. She has over 19 years of experience within Pharmaceutical R&D analytics and currently leads a broad array of assignments including Pharma R&D industry benchmarking, competitive landscape analysis and building business intelligence platforms within the R&D development space. She has extensive experience working with the business intelligence teams of leading pharmaceutical companies on topics such as R&D productivity , portfolio risk and protocol complexity. Prior to joining Clarivate, she has held a consulting role within the market research and data analytics team of a strategy consulting firm.

Jasmin has a Masters degree in Corporate Governance from Kingston University, London and a Bachelors degree in Economics and English Literature from the University of Mumbai.

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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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