Avicenna Alliance -
The ASBL aims at the improvement of public health through the promotion of in-silico medicine.
ID: 551058626331-14
Lobbying Activity
Response to Evaluation and revision of the general pharmaceutical legislation
27 Apr 2021
The Avicenna Alliance supports the Commission’s ambition to revise the EU’s general pharmaceuticals legislation as it represents an ideal opportunity to update the regulatory framework to reflect the uptake of new technologies, including in silico technologies such as Computer Modelling and Simulation (CM&S), for medicinal products including combination products and Advanced therapy medicinal products (ATMPs).
1. Unmet medical needs
When considering a new definition for ‘unmet medical needs’, the Commission needs to consider the benefits offered by CM&S for regulators in assessing the benefit-risk of medicines. By generating additional data in a short period for regulators and HTA bodies, in silico technologies can enable a better characterisation of these unmet medical needs, accelerate products development and approval, and allow for better prioritisation when considering access aspects.
The Avicenna Alliance calls on the Commission to consider in silico technologies as a relevant source of data to refine, reduce and occasionally replace clinical trials, accepted by both regulators and HTA bodies/ payers, as evidence for approval and access to innovative therapies in unmet medical needs such the field of medicines for children and rare diseases.
2. System of incentives
When evaluating and revising the general pharma legislation, the Commission should maintain the spirit of incentivisation, by clarifying for researchers and industry, the regulators’ requirements for acceptable in silico technologies application to demonstrate efficacy and safety of new treatments. The Avicenna Alliance stresses that academic and industry using in silico technologies, generating digital evidence of the efficacy and safety of treatments, should be incentivised and rewarded for doing so through the acceptance by regulators of this digital evidence.
3. Evidence generation tools for marketing authorisation of innovative medicines
In silico technologies allow us to leverage real-world evidence and generate digital evidence on the safety and efficacy of medicinal and combination products, leading to faster development overall. The performance of CM&S, including in silico trials, produces digital evidence, which is particularly interesting for paediatric and rare diseases, whereby evidence cannot be generated through in vitro, ex vivo or in vivo models, or is very challenging. .
The Avicenna Alliance calls on the Commission to urge regulators and HTA bodies to accept evidence, particularly leveraging real-world evidence, as this encourages rapid and more efficient product development, as well as better post-marketing data collection that informs benefit-risk.
4. Support and accelerate product development and authorisation
In silico trials enable the rapid and safe development and testing of medicinal products by speeding up the development pipeline through the prediction of therapeutic failure without exposing real patients and minimising the undesired effects. They can dramatically reduce time and costs linked to the development process including the number of clinical trials, and even complement ongoing clinical trials by performing modelling and simulations on virtual patient cohorts.
The Avicenna Alliance calls on the Commission to equip the EU with a regulatory framework acknowledging the emergence of in silico technologies and to establish a clear regulatory pathway for the use and acceptability of in silico trials in the context of medicines and combination products development in the EU.
Besides, the Avicenna Alliance asks the Commission to streamline the regulatory assets to include guidance for academia and industry to perform reliable in silico trials of the highest quality. There is a need for the Commission to develop harmonised common EU standards on ‘Good Simulation Practice’ to ensure the robustness and the quality of computer models in medicinal and combination products development.
Read full responseResponse to European Health Emergency Response Authority
24 Feb 2021
The Avicenna Alliance supports the Commission’s ambition to conduct horizon scanning to anticipate threats, identify countermeasures, generate and disseminate knowledge of the responses against cross-border threats. Computer Modelling and Simulation (CM&S) is decisive to identify and understand these threats, as in silico methodologies allow us to anticipate the spread of an infectious agent, to pinpoint the patients at risk, to forecast the disease spread and assess the overall threat. Computer models provide the power of analysis and prediction that enables us to measure disease impacts and to develop better intervention strategies.
The creation of a data infrastructure (European Health Data Space) collecting qualitative types of data - including demographic, immunological, clinical, pathological, imaging & genomics - is however, a key prerequisite to put in place for the development of qualitative computer models. The Commission hence needs to provide the necessary data infrastructure to seize the benefits offered by in silico methodologies and application so we better understand epidemics.
As the Commission takes up regulatory challenges and promotes advanced R&I and development of corresponding technologies and countermeasures, the Avicenna Alliance stresses that to ensure reliability, its industry (MedTech, Software, Biotech, Pharma companies) and academia members, as well as patients, are in need of regulatory guidance for the acceptance and applicability of CM&S in healthcare. The Avicenna Alliance calls on the Commission to provide regulatory guidance and develop harmonised common EU standards on ‘Good Simulation Practice’ to ensure the robust quality and working of computer models in healthcare.
By providing the regulatory clarity that researchers and industry need, the EU will enable in silico medicine and reap its benefits for setting up countermeasures. In particular, the EU should note that in silico clinical trials enable rapid & safe design and testing of medicines and devices against diseases, speeding up the development pipeline by predicting any therapeutic failure and minimising the undesired effects. It can also readily target sensitive and under-represented populations and operate under conditions where ethics render trials on humans difficult. The Avicenna Alliance asks the Commission to further encourage the use of in silico trials through the development of a common patient-specific models database, to form virtual cohorts for testing the safety, efficacy or performance of new preventive strategies, medicines and of medical devices.
Given that the Commission investigates the scalable manufacturing capabilities for the development of crisis-relevant countermeasures, the Avicenna Alliance stresses that CM&S can rapidly respond to challenges posed by the scaling up of medicines and vaccines production. CM&S methodologies can guide the proper design and operations of bioreactors and hence reduce the time needed to scale up and replicate the production of new treatments. The Avicenna Alliance invites the EU to use of CM&S when validating or guiding scalable manufacturing capacities and hereby enable rapid development of the medicines and vaccines manufacturing process from the pilot to manufacturing scale.
It is crucial to invest in training programmes to improve knowledge and skills in the most current techniques in biopharmaceutical science and bio-manufacturing. Moreover, there is also a need for investments in biomedical engineering, and new in silico technologies - such as AI, complex mechanistic and hypothesis driven models or hybrid techniques - to advance treatments. While it is key to promote the development of new medicines, the EU also needs to invest in medical equipment & medical devices (including software) for diagnostic and therapeutic care purposes. The Avicenna Alliance asks the HERA to support development and training programmes for the pharmaceutical and medical devices sectors.
Read full responseResponse to A European Health Data Space
3 Feb 2021
The European Health Data Space (EHDS) covering the exchange and access of primary and secondary use of good quality health data is eagerly awaited by the Avicenna Alliance to pursue scientific research and innovations, including the use of computer modelling and simulation (CM&S) in healthcare.
The Avicenna Alliance recognises the importance of privacy protection and security measures for health data and the need for harmonising the processing and sharing of health data, by requiring robust data integrity checks and interoperable data formats/standards. Besides, there is a need to boost data-driven R&D and innovations (e.g. in silico clinical trials) at industrial scales and address the unsolved challenge around the sharing of health data in a lawful GDPR-compliant way.
When considering the use of privacy protection measures, the Avicenna Alliance invites the Commission to consider the options offered by the so-called “visiting mode” and the generation of synthetic data as alternative data access-models preserving privacy.
About the interoperability of data, the Avicenna Alliance stresses the need to take into account the existing rules of international organisations (e.g., ICH, IMDRF, ISO, IHE, HIMSS, IEEE and HL7/FHIR) to limit disparities in the use of health data globally.
While technical developments, including in silico and AI techniques, enable us to turn the health data into actionable information, it is necessary to ensure the availability of as large as possible, high-quality data sets. This is of critical importance for cases such as rare diseases and paediatric diseases for which limited data is available and for which the use of in silico clinical trials can dramatically reduce the number of real patients involved.
When considering options for the support for training and testing of AI applications, the Avicenna Alliance asks the Commission to not only focus on ‘AI’ applications as a wide range of other technical developments, including in silico and modelling technologies, exist in healthcare.
The EHDS should not only have an important impact on the development of in silico technologies built on phenomenological models (e.g. purely data-driven such as AI) but also on mechanistic (e.g. hypothesis-driven) and hybrid models as they can drive health policies. More mechanistic in silico models have a major impact on the risk assessment and overall safety of the treatments and contribute to making more affordable new drugs and medical devices available by reducing their development, time and cost. The Avicenna Alliance calls the Commission to seize the establishment of a governance framework to support the training, testing, validation and exploitation of all in silico technologies.
The Avicenna Alliance welcomes the ambition to enhance the development, deployment and application of trustworthy digital health products and services, including those incorporating AI. While it is important to consider the options offered by AI, the Avicenna Alliance stresses the need to enhance technical developments including CM&S, digital twins, in silico clinical trials and high-performance computing.
As the Commission wants to support public authorities for accessing health data and supporting evidence-based decision-making, the Avicenna Alliance asks the Commission to encourage the use of Real-World Evidence and data produced by CM&S as part of HTAs and the EMA’s evaluation process.
In addition to the Commission's cautious approach to regulating the use of AI-based tools, in terms of safety, robustness, algorithm bias, accountability and fundamental rights, there is a need to regulate digital evidence produced by new techniques, including in silico medicine.
The Avicenna Alliance calls the Commission to consider how Good Simulation Practice (GSP) could offer the regulatory guidance that industry and researchers need to ensure the robust quality and working of computer models used in healthcare applications.
Read full responseResponse to Pharmaceutical Strategy - Timely patient access to affordable medicines
7 Jul 2020
1) Ensure greater access and availability of pharmaceuticals to patients:
Review the incentives for innovation by considering the role of computer modelling and simulation (CM&S) in core medicinal product legislation and promoting investment for medicines research and development in niche areas.
Take into account the contribution of digital evidence generated by CM&S in guiding treatment development, especially for paediatric and rare diseases, whereby evidence cannot be generated by the in vitro or ex vivo models, or that is very challenging to achieve based on in vivo (animal and patient) data.
Facilitate clinical data sharing to reduce the needs for placebo arms in clinical study and to develop in silico trials.
Improve patients’ safety and recovery by facilitating the use of CM&S that can determine the right treatment for the right patient at the right time.
Encourage and promote the adoption of in silico clinical trials to accelerate and to ease the development process of orphan drug development and customise treatment to paediatric patients.
Provide incentives for manufacturers of orphan medicinal products to augment their trial data with virtual trials can drastically enhance our understanding of the efficacity of these highly expensive treatments.
Introduce the requirement to perform in silico trials in the development process of paediatric medicines before testing on real patients. In silico trials can make possible that we move away from exploratory clinical trials in children limiting their need to confirmatory trials – to confirm that computer models can reliably predict safety and efficacy in children.
Promote the use of CM&S towards the adoption of personalised medicine.
2) Ensure affordability of medicines for patients and health systems sustainability:
Enable the use of Real World Evidence and of data produced by CM&S as part of Health Technology Assessment.
Improve the affordability of medicines by allowing the use of in silico trials to dramatically reduce the time and cost linked to development process and to the performance of clinical trials.
Encourage the use of CM&S as surrogate clinically relevant endpoints to complement and/or replace clinical studies aimed at obtaining price and reimbursement ensuring taxpayers only end-up payment for treatments that are truly safe and effective.
3) Enable innovation including for unmet medical needs in harnessing the benefits of digital and emerging science and technology:
Increase the support for CM&S in universities and spin-offs to break the cycle of monopoly for only developed treatments and allow smaller actors to assess whether their treatment could have a long commercial value.
Facilitate communication and networking between academic and industrial researchers.
Ensure the availability of funding mechanisms to allow greater collaboration between technology developers and end users;
Ensure the availability of data and mechanism for data sharing to support investigation for problems that can be solved using CM&S including AI.
Develop public databases of digital twins of people, products and healthcare in general.
Enhance participation to Prospective Payment System to accelerate development of innovations.
4) Reduce direct dependence on manufacturing from non-EU countries seek a level playing field for EU operators:
Enable the uptake of CM&S to improve the entire value chain from early stage discovery through manufacturing and distribution, offering greater scalability and flexibility in sourcing and meeting the requirements of regulatory bodies worldwide.
Develop financial incentives for industry to keep R&D and production within the EU.
Create standards and pathways for use of CM&S to evaluate specific medical products (like FDA MDDT).
Create a High Level Forum involving all stakeholders to find common solutions that would increase access to medicines.
Include the whole spectrum of CM&S in future legislative and funding schemes for the health sector
Read full responseResponse to Europe’s Beating Cancer Plan
3 Mar 2020
Prevention
The use of “digital tools” commensurately reflects the variety of different approaches that become
available. It should be emphasised that techniques to handle tumour profile data will not be limited
to genomics only (see the recent perspective on “deep phenotyping” tumours: https://www.nature.com/articles/s41571-019-0273-6 ) and that a variety of digital tools, including computer modeling and simulation, will allow for the integration of these data.
The Commission should therefore not only look into analysis of large sets of genomic tumour profiles but also into other techniques, including computer models.
Early Detection and Diagnosis (intervene early)
Artificial Intelligence has undoubtedly proven useful in current and future healthcare, despite potential ethical and legal issues for using AI in critical decision making (see e.g. https://academic.oup.com/ijlit/article-abstract/27/2/171/5485669?redirectedFrom=fulltext ).
In the current formulation, relatively low weight (“includes AI”) is put on AI as part of the set of digital tools. This low weight in a field where AI has its most pronounced strengths contrasts the relatively high weight of AI under “Knowledge, data and scientific evidence”. In this regard, the impact of AI on filling knowledge gaps has still not been finally clarified.
We suggest to enhance the weight of AI in the field of “Early detection and diagnosis” and complement AI with other modeling approaches in “Knowledge, data and scientific evidence”.
Knowledge, data and scientific evidence (Understanding cancer better)
We think that mentioning only AI as enabling technology to fill knowledge gaps (e.g. through cancer
genome integration) does not reflect the multi-faceted landscape of digital technologies in cancer
research and development.
AI - despite its strengths in recognition - can profit from the integration of inherently hypothesis/knowledge driven mechanistic modeling approaches. The necessity to understand and interpret the internal processes behind a prediction in order to become credible is widely acknowledged by the advent of explainable AI (https://www.sciencedirect.com/science/article/pii/S1566253519308103 & https://aapm.onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13891).
In the future, it is to expect that data-driven (AI) and hypothesis driven (mechanistic) models will converge to a common modelling paradigm (https://link.springer.com/article/10.1007/s00761-019-0624-z & https://www.nature.com/articles/s41746-019-0193-y)
Consequently, we believe that AI, allowing the fast processing of large amounts of health data, available through the 1+ Million Genomes Initiative should be combined with Mechanistic Modeling that allows the rationalisation of links between genomic and clinical data.
Read full responseMeeting with Annika Nowak (Cabinet of Commissioner Vytenis Andriukaitis) and RPP Group
8 Feb 2018 · Computer modelling in healthcare