governance Archives - European Industrial Pharmacists Group (EIPG)

Home  /  Generative AI in drug development  /  governance

A new member within EIPG


The European Industrial Pharmacists Group (EIPG) is pleased to announce the Romanian Association (AFFI) as its newest member following the annual General Assembly of EIPG in Rome (20th-21st April 2024). Commenting on the continued growth of EIPG’s membership, EIPG President Read more

The EU Parliament voted its position on the Unitary SPC


by Giuliana Miglierini The intersecting pathways of revision of the pharmaceutical and intellectual property legislations recently marked the adoption of the EU Parliament’s position on the new unitary Supplementary Protection Certificate (SPC) system, parallel to the recast of the current Read more

Reform of pharma legislation: the debate on regulatory data protection


by Giuliana Miglierini As the definition of the final contents of many new pieces of the overall revision of the pharmaceutical legislation is approaching, many voices commented the possible impact the new scheme for regulatory data protection (RDP) may have Read more

•••

Generative AI in drug development

March 11, 2024 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

by Giuliana Miglierini

Generative AI is perhaps the more advanced form of artificial intelligence available today, as it is able to create new contents (texts, images, audio, video, objects, etc) based on data used to train it. Applications of generative AI are not limited to, for example, the famous ChatGPT chatbot used to write complex texts, or to algorithms producing incredible images.

Generative AI is becoming a new paradigm in drug discovery, as it promises to greatly reduce both time and costs to develop new molecules, or to repurpose already existing ones for new indications. A fundamental goal for pharmaceutical companies, given that the average cost of developing a new medicines is estimated at $2.6 billion.

Algorithms can be trained on chemical-physical characteristics and 3D shapes of molecules in order to generate completely new molecules of interest for a certain application, and/or to predict their behaviour in the biological context (e.g. binding to a specific receptor). We resume the current status of implementation of generative AI in the field of drug development.

Quintillions of data
It seems ages since the first full sequencing of the human genome was completed in year 2000. Since then, vast amounts of genomic and other biological data have rapidly accumulated. To give an idea, the National Human Genome Research Institute estimates between 2 and 40 exabytes (i.e. quintillions) of data available within the next decade. The number becomes even more larger when considering other domains relevant to drug development, including chemical structures and properties, complex biochemical pathways, 3D protein structures and receptors, data on the efficacy and toxicity profile of already approved medicines and candidates in the pipelines, etc.
No matter to say, the parallel growing interest in artificial intelligence that characterised the last twenty years has turned fundamental for the availability of new technologies able to digest, extract and analyse these extremely large datasets.
Machine learning and deep learning algorithms represented just the first step towards this goal. Generative AI came as a consequence, its birth is attributed to a paper by Ian Goodfellow et al., published in 2014.

Opportunities and challenges of generative AI for drug discovery
The implementation of generative AI in the pharmaceutical and medtech sectors may lead to the an estimated economic value of $60-110 billion/year, says the report by McKinsey and Co. “Generative AI in the pharmaceutical industry: Moving from hype to reality”.
More specifically, McKinsey analysed 63 generative AI use cases in life sciences, calculating the potential economic impact for different domains. The higher values ($18-30 bln) are expected for the commercial domain, followed by research and early discovery ($15-28 bln) and clinical development ($13-25 bln). Less impacted appear enterprise ($8-16 bln), operations ($4-7 bln) and medical affairs ($ 3-5 bln).
Implementation of generative AI may prove not a so easy exercise for pharma companies, as it has to fit within an already complex organisation and with the strict regulatory requirements typical of the pharmaceutical lifecycle. An important message comes from the analysis from McKinsey: it is of paramount importance to exit the hype climate surrounding generative AI and understand exactly what it can and cannot be done.
The question is highly complex to be solved, and it requires multiple skills (data scientists, researchers, medical affairs, legal, risk and business functions) jointly working to set up the solution more suited to each company. The availability of a proper data infrastructure is just the first step, the chosen generative AI model has to be adapted to the complexity of the specific case of use, focusing on key applications to avoid disruption of the business.

According to an analysis by Boston Consulting Group, generative AI may prove useful to include also unstructured data among those used as data sources by the pharmaceutical industry. Possibly a challenging goal to achieve, as data access and management must fulfil regulatory requirements, for example in relation to the possibility to use data generated in clinical trials to support regulatory approval.
Governance of generative AI must also reflect the key principles established in the EU for AI systems, i.e. they “must be ‘safe, transparent, traceable, non-discriminatory and environmentally friendly,’ as well as ‘overseen by people, rather than by automation, to prevent harmful outcomes’.”

The need to integrate generative AI with human activities would probably call companies to redesign core processes. To this instance, selection of the more suited AI infrastructure and platform may turn critical for success of the initiative. Integration with already existing AI tools and flexibility are among other main features to be kept in mind. Not less important is the choice of the right partners, that should fit with the strategic business goals.

Many algorithms already available
The first AI applications based on deep learning algorithms were used, for example, to predict the sequence and structure of complex biological molecules. It was the case of the AlphaFold Protein Structure Database, which contains over 200 million protein structure predictions freely available to the scientific community. Other algorithms of this kind are ESMFold (Evolutionary Scale Modeling) and and Microsoft’s MoLeR, specifically targeted to drug design.
A more recent generation of generative AI are IBM’s MoLFormers UI, a family of foundation models trained on chemicals which can deduce the structure of molecules from simple representations. MoLFormer-XL screening algorithm, for example, was trained on more than 1.1 billion unlabelled molecules from the PubChem and ZINC datasets, each represented according to the SMILES notation system (Simplified Molecular Input Line Entry System). As reported by IBM, MoLFormer-XL is able to predict many different physical, biophysical and physiological properties (e.g. the capacity to pass the blood-brain barrier), and even quantum properties.

Mutual Information Machine (MIM) learning is the approach used by NVIDIA to built its MolMIM algorithms, a probabilistic auto-encoder for small molecule drug discovery. The NVIDIA BioNeMo cloud service uses these models to deploy a generative AI platform to create molecules that, according to the company, should fulfil all properties and features required to exert the desired pharmacological activity.

Not only big players: many new companies were born specifically to support the creation of generative (often end-to-end) AI platforms for drug discovery. Among the main ones, Insilico Medicine’s Pharma.AI platform is being used to build a fully self-generated pipeline comprehensive of 31 programs and 29 targets. The more advance product under development targets the rare disease idiopathic pulmonary fibrosis and is currently in Phase 2 in the US and China. The company’s inClinico AI data-driven multimodal platform to calculate the probability of success of single clinical trials proved useful to predict outcomes of Phase 2 to Phase 3 trials and to recognise weak points in study design.

UK’s based Exscientia, founded in 2012, is an AI-driven precision medicine company. Among its main achievements is the creation of the first functional precision oncology platform to successfully guide treatment selection and improve patient outcomes. The more advanced product in its pipeline is GTAEXS617, an oncology product targeting CDK7 in advanced solid tumors.
These are just few main examples, you can learn more on companies focused on AI for drug discovery in these articles published on Forbes and Pharmaceutical Technologies.


EMA’s reflection paper on AI in the pharmaceutical lifecycle

September 22, 2023 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

by Giuliana Miglierini

The rapidly evolving role of artificial intelligence (AI) and its possible application in the pharmaceutical field led the European Medicines Agency (EMA) to publish a draft Reflection paper on the use of AI along the entire lifecycle of both human and veterinary medicines.

The Reflection paper summarises the current view on the possible use of AI and machine leaning (ML) technologies and is open to public consultation until 31 December 2023. Comments, together with the outcomes of the joint HMA/EMA workshop to be held on 20-21 November 2023, will support its finalisation, as well as the possible development of new guidance. The initiative aims to identify the aspects of AI/ML use that would fall within the remit of EMA or of the single National Competent Authorities (NCAs) in charge of the dossiers’ assessment.

The document is part of the effort to improve the European Medicines Regulatory Network’s capability in data-driven regulation set forth by the joint HMA-EMA Big Data Steering Group (BDSG), and it has been developed in coordination with EMA’s CHMP and CVMP committees.

“The use of artificial intelligence is rapidly developing in society and as regulators we see more and more applications in the field of medicines. AI brings exciting opportunities to generate new insights and improve processes. To embrace them fully, we will need to be prepared for the regulatory challenges presented by this quickly evolving ecosystem” said Jesper Kjær, Director of the Data Analytics Centre at the Danish Medicines Agency and co-chair of the BDSG.

“With this paper, we are opening a dialogue with developers, academics, and other regulators, to discuss ways forward, ensuring that the full potential of these innovations can be realised for the benefit of patients’ and animal health” added Peter Arlett, EMA’s Head of Data Analytics and Methods, co-chair of the BDSG.

The main contents of the Reflection paper

The Reflection paper addresses the use of AI/ML with a dedicated chapter for each one of the steps, part of the overall lifecycle of a medicinal product. The interactions with regulators are then explored, as well as the different technical aspects to be taken into consideration. The final chapters discuss the governance of AI/ML applications, the integrity, and ethical aspects to be considered and how to address data protection.

The increasing use of AI technologies is based on the wide availability of big amounts of data produced and captured in electronic format on a routine basis. This data can be analysed by machine learning algorithms to inform the training of other systems able to analyse data and take actions with some degree of autonomy in order to achieve specific goals. These latter systems are the truly intended AI algorithms, those use is under discussion also in the field of regulatory decision making.

The Reflection paper recalls how the diffusion of these new technologies carries with it also some new risks, due to the “exceptionally great numbers of trainable parameters arranged in non-transparent model architectures”. Measures should thus be taken to ensure the safety of patients and the integrity of data, as well as to avoid the integration of bias into AI/ML applications.

The contents of the Reflection paper may also refer to combinations between a medicinal pro-duct and AI/ML-including medical device, as in such occurrence EMA is involved in the assessment of the characteristics of the device.

In general terms, existing guidelines, best practices, and recommendations relative to model in-formed drug development and biostatistics also apply to AI/ML; the adjacent methodology guidelines which may be relevant to this instance are listed in the document. The Reflection paper warns readers about the differences between the human and veterinary regulatory domains, suggesting that another reflection document specific for veterinary may be developed in future.

A risk-based approach

The usual risk-based approach typical of pharmaceutical development should be used to identify risks throughout the entire AI/ML tool lifecycle, including regulatory impact. According to the Reflection paper, the level of risk may depend also on the context of use and the degree of influence the AI technology exerts.

Developers should always seek early regulatory interaction and scientific advice, especially in cases where the AI/ML system may impact on the benefit-risk ratio of the medicinal product un-der development. To this instance, the EMA Innovation Task Force (ITF) is responsible for pro-viding early interaction on experimental technology, while scientific advice and qualification of novel methodologies in medicines development is provided by the Scientific Advice Working Parties (SAWP) of the CHMP for human medicines and of the CVMP for the veterinary ones.

As for all other steps of pharmaceutical lifecycle, the final responsibility for the suitability and use made of AI/ML algorithms, as well as their compliance to ethical, technical, scientific, and regulatory standards (GxP standards) and to current EMA scientific guidelines falls under the Marketing Authorisation Holders (MAHs) or applicants.

According to the Reflection paper, risks referred to in the discovery step may be limited, but they may impact the final evidence presented for regulatory review, thus principles for non-clinical development should be followed. This second step may include, for example, AI/ML modelling approaches according to the 3R principles. Regulators would expect to receive Standard Operating Procedures (SOPs) for AI/ML applications in preclinical studies; advisory documents on Application of GLP Principles to Computerised Systems and GLP Data Integrity should be also considered where appropriate. A pre-specified analysis plan would be needed for preclinical data potentially relevant for the assessment of the benefit-risk balance.

In the field of clinical trials, ICH E6 and VICH GL9 on human and veterinary GCPs should also be applied to the use of AI/ML. In particular, regulators expect the full model architecture and description of the data processing pipeline would be made fully available for comprehensive assessment should a model be generated for clinical trial purposes, as they would be considered parts of the clinical trial data or trial protocol dossier.

From precision medicine to pharmacovigilance

Particular considerations may be needed for applications of AI/ML in precision medicine, to individualise treatment according to the patient’s characteristics. AI/ML application supporting indication or posology decisions should be referenced in the Summary of product characteristics and their assessment falls under medicines regulation, representing a high-risk use both from the patient and regulatory perspectives. Close human supervision is also expected for AI applications used to draft or translate product information documents.

Many are the possible applications of AI/ML in the manufacturing of medicinal products, where they should follow the usual quality risk management principles (ICH Q8, Q9 and Q10) to guarantee safety, quality, and data integrity. New recommendations are also expected from EMA.

In the post-authorisation phase, AI/ML tools can effectively support efficacy and safety studies and post-marketing surveillance studies for veterinary medicines, as well as pharmacovigilance activities. Good pharmacovigilance practices should always be respected, and the used model should be validated, monitored and documented under the responsibility of the MAH. For conditional marketing authorisations, AI/ML applications should be discussed within a regulatory procedure unless details are agreed already at time of authorisation.


How to approach drug substance supply in new product introduction (NPI) processes

October 7, 2022 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

by Giuliana Miglierini

A key issue to be faced during pharmaceutical development refers to the supply of the active pharmaceutical ingredients and other raw materials to be used for the manufacturing of the first batches of investigational medicinal products, and then up to commercial production once approved.

Changes of specifications can frequently occur during experimentation, thus leading to the need to modify supply requirements for clinical programs. This is more true when dealing with biopharmaceutical investigational products, for which the traditional models for forecasting and demand processes may prove unfitted. The result is a lower robustness and predictability at early stages of the new product introduction (NPI) manufacturing processes. The complexity of the NPI supply chain is also impacting on manufacturing operations, with possible delays in the clinical program and launch schedule.

These issues have been addressed in the document “Guidelines for materials introduction supporting drug substance delivery”, published by the B2B organisation BioPhorum. A summary of its contents has been published in Bioprocess Online.

A good internal communication is fundamental

The ability to produce robust supply forecasts for new product introduction bases on a detailed knowledge of the planning of different activities to be run for a timely launch. Role and responsibilities have to be clear, as well as the information to be collected and timely shared between the manufacturing and commercial departments of biopharmaceutical companies.

The availability of such information is crucial to reduce the variability intrinsic in the NPI process for a biopharmaceutical product, which costs much more compared to a traditional smallmolecule based one. Reducing variability also impacts on the ability to better compete in the often highly dynamic market for biosimilars, or to address the launch of a new biotherapeutic under the correct perspective. Issues may be encountered also with respect to the regulatory approval processes, which may require different time lengths in different geographic areas or countries. This adds another uncertainty factor to estimates of the quantities of product to be manufactured.

Upon this considerations, the BioPhorum document identifies four key issues to be addressed to provide for a timely NPI process, including capacity and lead-time restrictions or oversupply, late change evaluation and implementation, governance issues and network complexity and in-licensed (or non-platform) products.

The availability of a good NPI process may avoid to incur many problems once operations are in place; all the needed master data information to support the use of raw materials should also be present and correct. BioPhorum’s suggestion is to include NPI processes in the creation of master service and supply agreements for the supply of raw materials, as they help to reach clarity on what a supplier can deliver and what it cannot.

A four steps methodology and roadmap

The document by the BioPhorum describes the results of a project aimed to develop a materialsbased methodology and roadmap to support improved NPI processes, on the basis of a collaborative industry approach to identify and implement best practices.

The result is a four steps process referring to the different activities needed to set up materials introduction and supply. The proposed different steps include the establishment of product lifecycle materials requirements, materials evaluation, supplier selection and qualification, and a manufacture and business review. Each of them should be supported by specific tools and checklists to be developed internally by the company. The governance of the process should involve senior supplier/manufacturer nominees to formally approve the package of deliverables at each stage gate.

Establishing product lifecycle material requirements

For each of the four steps of the NPI process, the BioPhorum document offers detailed lists of information to be collected and of expected outcomes.

Stage gate 1 addresses the establishment of product lifecycle material requirements, usually corresponding to the activation of first time in human studies (FTIH). Data to be collected include specifications of raw materials (e.g. order of magnitude, grade, supply options, environmental-health-safety (EHS) or geographic issues, etc.) as well as master data such as recipe information, plant diagram, list of equipment and process information. At the clinical level, information on the demand sensitivities on indication and clinical milestones and decision points should support the first estimates of the supply and demand plan, to be then expanded to agree on lifecycle forecasts.

The output may take the form of a ‘Product Lifecycle Demand and Supply Strategy’, a document discussing the long-term supply, demand and manufacturing of the product. Starting from the initial planning, the strategy should evolve through the creation of a data store specific for biopharmaceuticals, and the execution of gap analysis for in-licensed products. The strategy should also include a rough capacity modelling and description of ownership and the definition of a RACI matrix (responsible, accountable, consult, inform) to clarify roles and responsibilities with respect to each task, deliverable, or action. Information should be also available on high level technology requirements (both at the internal and external level). Strategic suppliers should be involved in early activities and materials risk analysis should be initiated.

Materials evaluation

Stage gate 2 refers to the information to be gathered from suppliers on the basis of requests for information (RFI) on materials. This should include all the different aspects relevant to the selection of the supplier, including capacity and costs, contacts, technical specifications and audit history, availability of samples, EHS aspects and business systems (e.g. availability of an appropriate ERP system).

This information should facilitate the identification of supplier that might be able to support the predicted or proposed growth of the product over its lifecycle. Stage gate 2 is also part of the risk management process to be run to validate the activation of full production.

Outputs include the sharing of forecasts and sensitivities with suppliers as needed, the establishment of a standard industrial master data set for biopharmaceuticals, as well as of business acceptance criteria.

Supplier selection and qualification

Stage gate 3 addresses the qualification process to finally select the most suitable suppliers and close the corresponding material supply agreements. The RFI and other information gathered in the previous step represent the basis of this exercise, aimed to develop a supply chain resilience strategic approach. The signature of the initial contracts is the final mark of formal selection, and should be supported by an agreement with the supplier on forecast and schedule for the supply, as well as of the business acceptance criteria.

Manufacture and business review

Stage gate 4 refers to the assessment of the operational performance of the supply chain for raw materials, a key activity in order to ensure continuity of supply and to promptly intercept any emerging issue on the basis of trends analysis.

Tools needed to this instance include the definition of appropriate metrics to monitor supplies (e.g. adherence to schedule, “On time in full”-OTIF, “Cost of poor quality”-COPQ). Information on the innovation potential of the supplier and the provision of a feedback on its performance is also deemed important. Any issue should be timely discussed between the supplier and the biopharmaceutical company, and confirmation of the production schedule agreed upon.


ACT EU’s Workplan 2022-2026

September 16, 2022 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

by Giuliana Miglierini

The implementation phase of the Accelerating Clinical Trials in the EU (ACT EU) initiative, launched in January 2022 by the European Commission, started with the publication of the2022-2026 Workplan jointly drafted by the Commission, the European Medicines Agency (EMA) and the Heads of Medicines Agencies (HMA).

The final target is to renew how clinical trials are designed and managed, so to improve the attractiveness of Europe for clinical research and the integration of results in the current practice of the European health system.

The 2022-2026 Workplan details the actions and deliverables planned according to the ten priorities identified by ACT EU. The drafting of the document took as primary reference also the recommendations of the European Medicines Regulatory Network (EMRN) strategy to 2025 and the European Commission’s Pharmaceutical Strategy for Europe.

Steps towards the full implementation of the CTR

The first priority of action should see the completion by the end of 2022 of the mapping of already existing initiatives within the EMRN and ethics infrastructure. This exercise represents a fundamental step to achieve a detailed picture of the current clinical trials regulatory landscape, characterised by the presence of various expert groups working in different areas.

The results of the mapping will form the basis to plan and implement a new strategy for the governance of the entire framework governing clinical trials, including the clarification of roles and responsibilities to the Network and its stakeholders. The expected outcome is the rationalisation and better coordination of the work done by different expert groups and working parties, as reflected by a new regulatory network responsibility assignment (RACI) matrix. The analysis and setting up of the new framework should start from the core governance bodies (Clinical Trials Coordination and Advisory Group (CTAG), Clinical Trials Coordination Group (CTCG), Commission Expert Group on Clinical Trials (CTEG) and Good Clinical Practice Inspectors Working Group (GCP IWG)), to then extend to other parts of the Network further.

The full implementation of the Clinical Trials regulation (Reg. (EU) 536/2014) by mean of the launch of monthly KPIs tracking of the planned activities is another key action. A survey to identify issues for sponsors and the consequent implementation of a process to prioritise and solve them are planned for the second half of 2022. The beginning of 2023 should see the launch of a scheme to better support large multinational clinical trials, particularly those run in the academic setting. One year later, at the beginning of 2024, a one-stop shop to support academic sponsors should also be launched.

An important action for the success of ACT EU should see the creation of a multi-stakeholder platform (MSP) to enable the interaction and regular dialogue of the many different stakeholders working in the field of clinical trials under different perspectives, both at the European and member state level. The platform should be launched by Q2 2023, with the first events run under its umbrella planned for Q3 and is expected to help in the identification of key advances in clinical trial methods, technology, and science.

Methodological updates in clinical trials

Another key step in the renewal of the European framework for clinical trials is linked to the updating of the ICH E6(R2) guideline on “Good Clinical Practice” (GCP). A targeted multi-stakeholder workshop on this theme is planned for Q1 2023, while the resulting changes should be implemented in EU guidance documents by Q3 2023. New GCPs should take into better consideration the emerging designs for clinical trials and the availability of new sources for data and are expected to “provide flexibility when appropriate to facilitate the use of technological innovations in clinical trials”. This action also includes the development of a communication and change management strategy to support the transition to the revised GCP guideline, and the updating of other relevant EU guidelines impacted by the change.

The opportunity to introduce innovative clinical trial designs and methodologies shall be addressed starting from decentralised clinical trials (DCT), with the publication of a DCT recommendation paper by the end of 2022. A workshop on complex clinical trials should be also organized to discuss issues linked to study design, such us umbrella trials and basket trials or master protocols. New technologies may support innovative approaches to the recruitment of eligible study participants and new ways to capture data during clinical trials. The publication of key methodologies guidance is an expected deliverable, together with a improved link between innovation and scientific advice.

A new EU clinical trials data analytics strategy is expected to be published by the end of 2022, while the first half of next year should see the development of a publicly accessible EU clinical trials dashboard and a workshop to identify topics of common interest for researchers, policy makers, and funders. These activities are targeted to fully exploit the opportunities offered by data analytics, so to identify complex trends from the large base of data about clinical trials collected by the EMRN. The existence of multiple data sources is a main barrier currently affecting the possibility to access, process and interpret these data.

Another priority is to plan and launch a targeted communication campaign to engage all enablers of clinical trials, including data protection experts, academia, SMEs, funders, Health Technology Assessment (HTA) bodies and healthcare professionals. Up to 2024, this action will also support sponsors in remembering the importance of training linked to the application of the CTR and the mandatory use of the Clinical Trials Information System (CTIS). All other communication needs across all priority actions will also be handled under this action.

Scientific advice, safety monitoring and harmonised training

The current framework sees the involvement of different actors who interact with sponsors at different stages of product development to provide them with scientific advice. A simplification of the overall process should be pursued by grouping of key actors in clinical trials scientific advice in the EU, “with the aim of critically analysing the existing landscape in line with stakeholder needs”. The Workplan indicates several pilot phases should be run to identify the better way to address this topic, which should benefit especially academic or SMEs sponsors that may have less experience of regulatory processes. Planned activities include a enhanced intra-network information exchange, the running of a survey among stakeholders and the operation of a first pilot phase by Q4 2024, to then optimise and expand the advice process upon results.

The establishment of clinical trial safety monitoring is another central theme of action, that should see member states involved in a coordinated work-sharing assessment. Key activities should include the identification of safe CT KPIs by the end of 2022 and a review of IT functionalities for safety, and it will be run in strict connection with the EU4Health Joint Action Safety Assessment Cooperation and Facilitated Conduct of Clinical Trials (SAFE CT). Training of safety assessors and the development of a harmonised curriculum thereof shall be also considered, as well as the alignment of safety procedures for emerging safety issues potentially impacting clinical trials.

The development of a training curriculum informed by regulatory experience should support the creation of a renewed educational ‘ecosystem’ characterised by bidirectional exchanges to enable training on clinical trials. This action is target mainly to better engage universities and SMEs, and it should include also training provided by actors other than the regulatory network.


EIC: challenges for the governance and opportunities for innovation

July 1, 2022 , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,

by Giuliana Miglierini

The European Innovation Council (EIC) was launched in March 2021 by the EU Commission to support the growth of highly innovative startup companies. Since then, the programme experienced some difficulties to become fully operative, as delays occurred with companies requesting grant-only or grant-first support and with the decision-making procedures for companies requesting blended finance or equity-only investments.

According to the Commission, this situation is a result of the restructuring of the EIC Fund to better reflect Horizon Europe legislation and the outcomes of the pilot phase. Negotiations are also ongoing with an external fund manager of the EIC Fund and are expected to close by the end of June. Internal discussions in the European Commission and IT problems are among the possible causes of the delays, reported Politico. A situation that is highly impacting on the selected companies, that are hampered from proceeding with the timely development of their business.

The difficult governance of the EIC prompted the European Parliament to start an investigation, led by Horizon Europe’s rapporteur Christian Ehler, to better clarify the issues undermining the EIC functioning (see more on ScienceBusiness). Mr. Ehler asked the stakeholders to provide inputs by 14 June; the final outcomes of the investigation will be summarised in a non-legislative report on the implementation of the EIC.

“The idea behind the report is to get the debate about the future of the EIC out in the open and provide the Parliament’s perspective on it. As co-legislator we have a duty to ensure the Commission implements the legislation we approved,” said Christian Ehler.

The EIC Accelerator

Available investments for startups and SMEs under the EIC Accelerator programme total €2.5 million for grants and €0.5 to €15 million equity investments through the EIC Fund. Higher investments are possible to support the development of technologies of strategic European interest.

A fast assessment procedure was introduced in 2021 to submit new projects at any time. A tailored business coaching support is available to successful candidates to draft the full applications, which are then evaluated at regular cut-off dates approximately every three months. The Commission announced it is finalising its decision-making procedure for the grant and equity components to companies selected for blended finance during the 2021 cut-offs. This is expected to allow the signature of contracts for the grant component of blended finance in a couple of days after the closure of the decision-making procedure, followed by the payment of a pre-financing of the grant one week later. A due diligence is needed to support the investment decision by the EIC Fund for the equity component, that will thus occur few weeks or months later.

The current status of the EIC Accelerator

According to the European Commission, 65 companies were selected for funding under the EIC Accelerator programme for the June 2021 cut-off, following the evaluation of their full application. Of these, 29 companies requested grant-only or grant-first support and 31 requested blended finance, including a grant component and equity investment. Contracts for six grant-only or grant-first companies were still to be signed as of 13 May 2022. The grant component is expected to close by early June 2022, while for the equity investment component and equity-only closure of the investment agreement is expected after June.

Some other 99 companies were selected for support in the October 2021 cut-off. Only one contract of the overall 34 companies that requested grant-only or grant-first support has been signed. Signature of the grant component for companies that selected blended finance is planned in July 2022, followed by the equity component and equity-only projects from the summer up to the end of the year.

The third cut-off round of March 2022 saw the selection of some other 74 companies, over a total of more than 1000 applications. Selected companies will each receive grants and/or equity investments up to €17.5 million. The next cut-offs for full applications is 15 June and 7 October.

Deep-tech training needed

A report published in April 2022 by the EIC Pilot Expert Group suggests the creation of two new deep-tech training programmes to better support the development of human entrepreneurial talent while fostering technological solutions. “We argue that EIC can’t succeed without including in its mandate the objective of proactively realising the entrepreneurial talent of Europe’s brilliant scientists”, write the members of the Expert Group in the foreword of the document.

The EIC Trailblazer Programme and the Pioneer Programme are the tools identified to reach this challenging goal. Both of the programmes should be implemented in a phased manner using pilot projects to allow for experimentation and learning, according to the recommendations set forth in the report. A main expected outcome is the creation of a new generation of deep-tech entrepreneurs, the EIC Innovators, able to better evaluate how their technologies are fitting into the world for commercialisation and impact.

The EIC Trailblazer Programme is targeted to support talented PhD candidates and postdocs that are part of projects funded by the EIC Pathfinder and EIC Transition. These EIC Trailblazer Fellows may receive a deep tech training programme, aimed to work as an internal accelerator and an elite programme targeting proto-entrepreneurs. A special prize and/or grant may also be considered to recognise scientific and entrepreneurial talents.

The Pioneer programme would allow for deep-tech add-on modules sponsored by the EIC to complement existing programmes delivered at the local level, in member states and potentially EU associated countries. Beneficiaries would include talented scientists that one day may apply for EIC funding, the “proto-EIC Innovators”.

Comments from research-intensive universities

The Guild of European research-intensive universities published a statement to contribute to MEP Christian Ehler’s initiative of a report on the implementation of the EIC. A better recognition of the role of universities’ Technology transfer offices (TTOs) as key actors in enabling researchers to develop their results for commercial and societal purposes is the key message of the Guild. To this instance, duplication of activities of the TTOs in terms of project management and support services should be avoided. Concerns are also highlighted with reference to the standard Intellectual Property (IP) provisions in the EIC Pathfinder and Transition schemes, as they might negatively affect the functioning of already well-performing TTOs without strengthening the capacities of weaker TTOs.

A positive experience is also acknowledged as for the EIC Transition scheme, that supports universities and their spin-offs with appropriate financial support for proof-of-concept projects. The Guild asks for the extension of this funding scheme to support an higher number of innovative projects.

An example of funded project

Swedish company Bico (formerly Cellink) is an example of EIC-funded project which saw a very rapid growth of its business, achieving $ 1 billion in market valuation in the first five years of activity. Founded in 2016, the company is now leader in the bioink sector and is developing new bio-printing technologies to be used for 3D printing of organs and tissues, so to overcome the lack of donors, reduce shortages and improve drug development.

Bioprinting is only one of the technologies included in Bico’s portfolio; gene therapy, gene editing, CRISPR, diagnostics are also investigated. The company built up from the first universal bioink created by Professor Paul Gatenholm (Chalmers University), a special biomaterial that enables human cells to grow outside the body and perform all the vital functions.