Key drivers of innovativeness appraisal for medicines: the Italian experience after the adoption of the new ranking system

Galeone, Carlotta; Bruzzi, Paolo; Jommi, Claudio

doi:10.1136/bmjopen-2020-041259

Cited by 14 publications

(21 citation statements)

References 17 publications

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“…As regards the UMN, a five-point scale according to the 2017 AIFA criteria was adopted. 8 , 10 , 13 Details about the scale are reported in Galeone et al. 8 The AB evaluation was carried out according to the version 1.1 (v1.1) of the ESMO-MCBS criteria.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, for indications considered innovative before 2017, QE was evaluated according to the GReFO (Regional Group of Oncological Drugs) of the Emilia Romagna region, based on the Grades of Recommendation Assessment, Development and Evaluation (GRADE) method. 8 , 17 , 18 According to this scale, the quality of evidence can be scored as High (i.e. further research is very unlikely to change our confidence in the estimate of effect), Moderate (i.e.…”

Section: Methodsmentioning

confidence: 99%

“…As regards the UMN, a five-point scale according to the 2017 AIFA criteria was adopted. 8,10,13 Details about the scale are reported in Galeone et al 8 The AB evaluation was carried out according to the version 1.1 (v1.1) of the ESMO-MCBS criteria. 5,16 Finally, for the QE parameter, the AIFA assessments for all indications considered innovative since 2017 were adopted.…”

Section: Data Generation and Analysismentioning

confidence: 99%

“…further research is very unlikely to change our confidence in the estimate of effect), Moderate (i.e. further research is likely to have an important impact on our confidence in the estimate of effect and 5,16 High ( 5) Important ( 4) Moderate ( 3) Scarce ( 2) Absent (1) Quality of evidence 8,17 High Moderate Low Very Low may change the estimate), Low (i.e. further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate) and Very Low (i.e.…”

Section: Data Generation and Analysismentioning

confidence: 99%

“…In 2017, AIFA identified the criteria for classification of innovative drugs focusing on therapeutic need, added therapeutic value and quality of evidence. 7 , 8 , 9 , 10 While previous experience in other European countries explored the relationship between the added therapeutic value and price definition of anticancer drugs for solid tumors, 11 no evidence is available on reproducible multi-parameter integrated criteria for early access (EA) priority classification of anticancer drugs both for solid and hematological tumors. Moreover, homogeneous criteria for the evaluation of clinical benefit are still not available for both solid oncology and hematology: the first being firmly anchored to the ESMO criteria, and the second currently lacking a similar methodological tool.…”

Section: Introductionmentioning

confidence: 99%

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Skip pattern approach toward the early access of innovative anticancer drugs

et al. 2021

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Background With the rapid development of innovative anticancer treatments, the optimization of tools able to accelerate the access of new drugs to the market by the regulatory authority is a major issue. The aim of the project was to propose a reliable methodological pathway for the assessment of clinical value of new therapeutic innovative options, to objectively identify drugs which deserve early access (EA) priority for solid and possibly in other cancer scenarios, such as the hematological ones. Materials and methods After a comprehensive review of the European Public Assessment Report of 21 drugs, to which innovation had previously been attributed by the Italian Medicines Agency (Agenzia Italiana del Farmaco, AIFA), an expert panel formulated an algorithm for the balanced use of three parameters: Unmet Medical Need (UMN) according to AIFA criteria, Added Benefit (AB) according to the European Society for Medical Oncology’s Magnitude of Clinical Benefit Scale (ESMO-MCBS) criteria and Quality of Evidence (QE) assessed by the Grades of Recommendation Assessment, Development and Evaluation (GRADE) method. By sequentially combining the above indicators, a final priority status (i.e. EA or not) was obtained using the skip pattern approach (SPA). Results By applying the SPA to the non-curative setting in solid cancers, the EA status was obtained by 5 out of 14 investigated drugs (36%); by enhancing the role of some categories of the UMN, additional 4 drugs, for a total of 9 (64%), reached the EA status: 2 and 3 drugs were excluded for not achieving an adequate score according to AB and QE criteria, respectively. For hematology cancer, only the UMN criteria were found to be adequate. Conclusions The use of this model may represent a reliable tool for assessment available to the various stakeholders involved in the EA process and may help regulatory agencies in a more comprehensive and objective definition of new treatments’ value in these contexts. Its generalizability in other national contexts needs further evaluation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Generation and Analysismentioning

confidence: 99%

Section: Data Generation and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Skip pattern approach toward the early access of innovative anticancer drugs

et al. 2021

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Pricing, Procurement and Reimbursement Policies for Incentivizing Market Entry of Novel Antibiotics and Diagnostics: Learnings from 10 Countries Globally

Vogler,

Habimana,

Haasis

et al. 2024

Appl Health Econ Health Policy

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The Evaluation of Drug Innovativeness in Italy: Key Determinants and Internal Consistency

Jommi

Galeone

2023

PharmacoEconomics Open

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Background Innovative medicines are provided with dedicated funds and immediate market access in Italy. Innovativeness evaluation considers unmet need, added therapeutic value, and quality of the evidence. Objective We aimed to evaluate the internal consistency and drivers of the innovativeness appraisal process. Methods Appraisal reports on innovativeness refer to 1997–2021. We used both a descriptive approach and probabilistic multivariate analysis, using logistic regression models to compute odds ratios and 95% confidence intervals. The dependent variable is innovativeness status (innovative vs. non-innovative; full innovativeness vs. conditional innovativeness). Explanatory variables, besides the three above-mentioned domains, are the year of evaluation, drug type, target disease and population, and the number and type of available studies. Results Among the 141 medicines scrutinized, 31.9%, 29.8%, and 38.3% were evaluated as fully innovative, conditionally innovative, and non-innovative, respectively. Added therapeutic value and the quality of the evidence were associated with the odds of receiving innovative status, and full compared with conditional innovativeness; unmet need was not a predictive variable. Other factors played a minor role: medicines for both solid tumours and rare diseases are more likely to be judged innovative; conditional innovativeness is more probable for medicines for rare diseases. Conclusions Innovativeness status is driven by the added therapeutic value and quality of evidence. The appraisal process is internally consistent and predictable. This provides industry with a clear indication of what is needed to ensure that access to their medicines is prioritized.

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Key drivers of innovativeness appraisal for medicines: the Italian experience after the adoption of the new ranking system

Cited by 14 publications

References 17 publications

Skip pattern approach toward the early access of innovative anticancer drugs

Skip pattern approach toward the early access of innovative anticancer drugs

Pricing, Procurement and Reimbursement Policies for Incentivizing Market Entry of Novel Antibiotics and Diagnostics: Learnings from 10 Countries Globally

The Evaluation of Drug Innovativeness in Italy: Key Determinants and Internal Consistency

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