Technology & Research Archives - European Industrial Pharmacists Group (EIPG)

The Pact for Research and Innovation in Europe


by Giuliana Miglierini The roadmap to support the implementation of the new vision of the European Research Area (ERA) made a concrete step forward on 16 July 2021, with the adoption by the European Commission of the proposal for a Read more

The opportunity for repurposing of oncology medicines


by Giuliana Miglierini Rare cancers, which account for approx. 22% of new cases in Europe, represent an area of low business interest for the pharmaceutical industry, due to the limited number of patients compared to the very high costs to Read more

A golden era for UK’s life sciences and a new Code of practice for its pharmaceutical industry


by Giuliana Miglierini Less than a year has gone since the Brexit, and the UK innovation landscape is experiencing a new, vivid era of expansion under the stimulus of a strong demand from global investors. According to recent data of Read more

The opportunity for repurposing of oncology medicines

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by Giuliana Miglierini

Rare cancers, which account for approx. 22% of new cases in Europe, represent an area of low business interest for the pharmaceutical industry, due to the limited number of patients compared to the very high costs to develop targeted treatments. It is thus important to consider the possibility for already existing medicines to be repurposed for a new indication. Lower costs of development and risk of failure, and a shorter time frame to reach registration are upon the main advantages of repurposing compared to de novo development, highlights the Policy Brief presented during the Joint meeting of EU Directors for Pharmaceutical Policy & Pharmaceutical Committee of 8 and 9 July 2021.
The experts addressed more specifically the possibility to achieve non-commercial repurposing of off-patent cancer medicines, which are commonly used off-label to treat patients not responsive to other more innovative types of therapies.

The issue of non-commercial development
The request of a new indication for an already marketed medicine has to be submitted by the Marketing authorisation holder (MAH). This greatly hampers the access to noncommercial repurposing by independent research institutions, as they would need to find an agreement with the MAH, the only responsible for all the interactions with regulatory authorities, at the central (EMA) or national level.
Considering the issue from the industrial point of view, this type of external request may prove difficult to be answered positively, when taking into consideration the very low return on investment that can be expected from a repurposed off-patent medicine. Even EU incentives schemes, such as those on data exclusivity and orphan designation, may not be sufficiently attractive for the industry. Current incentives schemes, for example, allow for an additional year of exclusivity in case of a new indication for a well-established substance, a 10-year market exclusivity
plus incentives in case of an authorised medicine granted with orphan designation, or the extension of the supplementary protection certificate for paediatric studies (plus 2 years market exclusivity for orphans).
The following table summarises the main issues and potential solutions involved in the setting of a specific reference framework for the repurposing of off-patent medicines for cancer, as reported in the WHO’s Policy Brief.

Table: Short overview of issues and solutions in repurposing of off-patent medicines for cancer
(Source: Repurposing of medicines – the underrated champion of sustainable innovation. Copenhagen: WHO Regional Office for Europe; 2021. Licence: CC BY-NC-SA 3.0 IGO)

Many projects active in the EU
The European Commission started looking at the repurposing of medicines with the 2015-2019 project Safe and Timely Access to Medicines for Patients (STAMP). A follow-up phase of this initiative should see the activation in 2021 of a pilot project integrated with the new European Pharmaceutical Strategy.
Several other projects were also funded in the EU, e.g. to better train the academia in Regulatory Science (CSA STARS), use in silico-based approaches to improve the efficacy and precision of drug repurposing (REPO TRIAL) or testing the repurposing of already marketed drugs (e.g. saracatinib to prevent the rare disease fibrodysplasia ossificans progressive, FOP). A specific action aimed to build a European platform for the repurposing of medicines is also included in Horizon Europe’s Work programme 2021 –2022; furthermore, both the EU’s Beating Cancer Plan and the Pharmaceutical Strategy include actions to support non-commercial development for the repurposing of medicines.

According to the WHO’s Policy Brief, a one-stop shop mechanism could be established in order for selected non-commercial actors, the so-called “Champions”, to act as the coordination point for EU institutions involved in the funding of research activities aimed to repurposing. This action may be complemented by the support to public–private partnerships involving research, registration and manufacturing and targeted to guarantee volumes for non-profitable compounds.
Among possible non-profit institutions to access funding for repurposing research in cancer are the European Organisation for Research on Cancer (EORTC) and the Breast Cancer International Group. An overview of other existing initiatives on repurposing has been offered during the debate by the WHO’s representative, Sarah Garner.

How to address repurposing
Looking for a new indication is just one of the possible points of view from which to look at the repurposing of a medicine. Other possibilities include the development of a new administration route for the same indication, the setup of a combination form instead of the use of separated medicinal products, or the realisation of a drug-medical device combination.
A change of strategy in the war on cancer may be useful, according to Lydie Meheus, Managing Director of the AntiCancer Fund (ACF), and Ciska Verbaanderd.
Keeping cancer development under control may bring more efficacy to the intervention than trying to cure it, said ACF’s representatives. The possible approaches include a hard repurposing, with a medicine being transferred to a completely new therapeutic area on the basis of considerations about the tumor biology and the immunological, metabolic and inflammatory pathways, or a soft repurposing within the oncology field, simply looking to new indications for rare cancers.
From the regulatory point of view, a possible example for EMA on how to address the inclusion of new off-label uses of marketed medicines is given by the FDA, which may request a labeling change when aware of new information beyond the safety ones.

The Champion framework
The Champion framework, proposed as a result of the STAMP project, is intended to facilitate data generation and gathering compliant to regulatory requirements for a new therapeutic use for an authorised active substance or medicine already free from of intellectual property and regulatory protection.
A Champion is typically a not-for-profit organisation, which interacts with the MAH in order to include on-label what was previously off-label, using existing regulatory tools (e.g innovation offices and scientific and/or regulatory advice). The Champion shall coordinate research activities up to full industry engagement and would be responsible for filing the initial request for scientific/regulatory advice on the basis of the available data. The pilot project to be activated to test the framework will be monitored by the Repurposing observatory group (RepOG), which will report to the Pharmaceutical Committee and will issue recommendations on how to deal with these types of procedures.

AI to optimise the chances of success
Artificial intelligence (AI) may play a central role in the identification of suitable medicines to be repurposed for a target indication, as it supports the collection and systematic analysis of very large amounts of data. The process has been used during the Covid pandemic, for example, when five supercomputers analysed more than 6 thousand molecules and identified 40 candidates for repurposing against the viral infection.
AI can be used along drug development process, making it easier to analyse the often complex and interconnected interactions which are at the basis of the observed pharmacological effect (e.g drug-target, protein-protein, drug-drug, drug-disease), explained Prof. Marinka Zitnik, Harvard Medical School.
To this instance, graphic neural networks can be used to identify a drug useful to treat a disease, as it is close to the disease in “pharmacological space”. The analysis may also take into account the possible interactions with other medicines. This is important to better evaluate the possible side effects resulting from co-prescribing; annual costs in treating side effects exceed $177 billion in the US alone, according to Prof. Zitnik.


The Swiss interoperable national eHealth infrastructure

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by Giuliana Miglierini

The new model of a personalised and interconnected healthcare asks for the interoperability of data in order to precisely access all the information needed to make the correct diagnosis and decide the most appropriate treatment for each patient.

Interoperability is at the core of the new Swiss strategy used to build the national eHealth infrastructure; the strategy has been developed by a team of scientists from the University of Geneva (UNIGE) and the University Hospitals of Geneva (HUG), in collaboration with the Swiss Institute of Bioinformatics (SIB) and the Lausanne University Hospital (CHUV), under the auspices of the Swiss Personalized Health Network (SPHN) and in close collaboration with representatives from all five Swiss university hospitals and eHealth Suisse.

A journey started in 2015

The new national infrastructure strategy will be adopted by all Swiss university hospitals and academic institutions. The announcement of the new strategy follows a long-lasting work to adequate the Swiss legislation, started in 2015 with the approval of the new federal law on patients’ electronic health records (EHR) (see more on Health Policy).

According to the Swiss law (entered into force in April 2017), adoption of the interoperable infrastructure is voluntary for ambulatories and private practices. In the same year, the Swiss Personalized Health Network (SPHN) also created by the government, an initiative led by the Swiss Academy of Medical Sciences in collaboration with the SIB.

Despite major investments over the past decade, there are still major disparities”, says Christian Lovis, director of the Department of Radiology and Medical Informatics at the UNIGE Faculty of Medicine and head of the Division of Medical Information Sciences at the HUG. “This is why we wanted, with our partners and the SPHN, to propose a strategy and common standards that are flexible enough to accommodate all kinds of current and future databases.”

A semantic framework integrating with the existing standards

The new infrastructure will be implemented to complement the existing tools already used by the Swiss eHealth community. Synergy and flexibility are the principles that inspired its development, which is based on a common semantic framework that does not aim to replace existing standards. The final target is to make a step forward towards the application of personalized medicine, so to better respond to the needs of both patients and the Swiss healthcare system. The new infrastructure has been officially presented by an article published in the JMIR Medical Informatics.

Its stepwise implementation has already started at mid-2019, within the framework of the Swiss Personalized Health Network. “Swiss university hospitals are already following the proposed strategy to share interoperable data for all multicentric research projects funded by the SPHN initiative”, reports Katrin Crameri, director of the Personalized Health Informatics Group at SIB in charge of the SPHN Data Coordination Centre. Some hospitals are also starting to implement this strategy beyond the SPHN initiative.

In the JMIR Medical Informatics article, the authors describe the process that led to the new strategy, starting from the deep analysis of various approaches to interoperability, including Health Level Seven (HL7) and Integrating Healthcare Enterprise (IHE). Several domains have been also addressed, including regulatory agencies (e.g. Clinical Data Interchange Standards Consortium [CDISC]), and research communities (e.g. Observational Medical Outcome Partnership [OMOP]).

The semantics of the infrastructure was mapped according to different existing standards, such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), the Logical Observation Identifiers Names and Codes (LOINC), and the International Classification of Diseases (ICD).

A resource description framework (RDF) allows for the storing and transportation of data, and for their integration from different sources. Data transformers based on SPARQL query language were implemented to convert RDF representations to the required data models.

A common semantic approach

The three pillars on which is built the new infrastructure reflect the three essential components of communication: the commonly shared meaning we give to things, a technical standard producing the “sound” and the organisation of the meaning and sound with sentences and grammar so that communication becomes intelligible. The same occurs with data, where the agreed semantic significant is used to represent conceptually what has to be communicated. “Then we need a compositional language to combine these meanings with all the freedom required to express everything that needs to be expressed. And finally, depending on the projects and research communities involved, this will be ‘translated’ as needed into data models, which are as numerous as the languages spoken in the world”, explains Christophe Gaudet-Blavignac, a researcher in the UNIGE team.

Unification of vocabularies instead of creation of new ones has been a major target for scientists involved in the effort; this new common vocabulary will be now used to communicate within any type of grammar, without need to learn a ‘new language’. “In this sense, the Swiss federalism is a huge advantage: it has forced us to imagine a decentralised strategy, which can be applied everywhere. The constraint has therefore created the opportunity to develop a system that works despite local languages, cultures and regulations” says Christian Lovis.

This approach is expected to provide a robust guarantee of mutual understanding and significant time savings for researchers called to prepare relevant documentation, as specific data models will be applied only as the last step of the procedure. The chosen modalities shall provide the needed flexibility to adapt to the formats required by a particular project, for example those typical of the FDA in the case of collaboration with an American team.

The challenges of interoperability

The new infrastructure takes also into due account the many challenges related to the sharing of data. Instruments that create interoperability and their implementation have to face the regulatory framework that governs data accessibility and protection, for example with reference to the GDPR regulation on personal data. “The banking world, for example, has long since adopted global interoperability standards, – comments Christophe Gaudet-Blavignac. – A simple IBAN can be used to transfer money from any account to any other. However, this does not mean that anyone, be they individuals, private organisations or governments, can know what is in these accounts without a strict legal framework

Interoperability is even more a challenging goal to be achieved in the biomedical field, due to the very great heterogeneity of data involved in the diagnosis and treatment of a certain disease, and the consequent need to interconnect and integrate many different systems to achieve a robust communication. This issue has been made fully explicit during the pandemic, when a huge amount of data of different types were produced: even if lifting all technical, legal and ethical constraints to their interoperable use, the data remain difficult to analyse because of semantic ambiguities, notes the Swiss scientists.

Big data and new technologies

The digital opportunity in the Swiss healthcare system has been also examined by PricewaterhouseCoopers (PwC) in a report of February 2019. Many new informatics technologies may prove useful to boost the eHealth Swiss landscape, suggest the analysts, from the use of big data and data management to the spreading of wearable devices and sensors among patients.

According to PwC, the first ones are expected to transform the diagnosis process from a subjective experience to an objective, data-driven process. This would allow also to improve its transparency, providing a rationale for the choice and effectiveness of treatments.

Wearables and sensors are expected to further expand this vision to self-diagnosis, monitoring and remote treatment, thus supporting the transition towards a prevention-based healthcare industry pursuing very early-stage identification of pathologies and related therapeutic interventions.

The PwC’s study – comprehensive of 38 interviews with patients and industry experts – ran in collaboration with the University of St Gallen. Six different categories of patients were identified: the Health enthusiast, the Sceptic, the Healthy Family, the Chronic, the Frail elderly and the Mentally stressed. For each of them, a map identifying pain points along the patient journey were also derived in relation to the domains of Time, Emotions, Information and Resources.

Lack of trust in the healthcare system, insufficient availability and accuracy of resources and the time is spent in waiting rooms are among the main issues experienced by Swiss patients, according to PwC. All of them can be tackled using the new digital technologies, including big data, wearables and sensors, artificial intelligence, robotics, telemedicine and mobile health, digital simulation, body augmentation and remediation.


Small-scale models for process development

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by Giuliana Miglierini

There are many steps within a pharmaceutical production that may require the availability of a model of the manufacturing process in order to run targeted simulations. To this instance, a useful approach is represented by the so-called “small-scale models” (SSMs, or “scaled-down models”), that are usually developed to reflect the real working parameters available for a certain large manufacturing facility.
A small-scale model needs to undergo a process qualification (SSMQ) in order to be acceptable from the regulatory point of view. The main features and criticalities of SSMQ have been discussed in a series of articles published on BioProcess Online, and based on the results of a survey run between the representatives of large biopharmaceutical companies participating to the BioPhorum Development Group. A white paper on SSMs is also available.

The main requirements for an SSM
A critical requirement for a small-scale model to be accepted by regulators is its ability to exactly replicate the large-scale manufacturing process. This can be assessed and justified by choosing appropriate process parameters to be used as inputs for the simulation and obtaining outputs showing performance and quality attributes comparable to the large-scale process.
Small-scale models can be used both in early development, for example to support clinical manufacturing, and in late-stage development (e.g. to identify critical process parameters).
The overall quality of the model increases in the passage from early- to late-stage applications, due to the increasing number of data available to simulate the processes. Alternatively, a scientific evaluation of the process without application of a formal statistical method might be used, but a good experience and sufficient platform knowledge is needed in order to obtain valid results.
Other examples of the utility of SSMs in biopharmaceutical manufacturing include media stability and cell line stability studies, qualification of raw materials, impurity clearance validation, postapproval process changes and resolution of deviations.
The clearing of infectious viruses is a particularly critical step in biomanufacturing, and it should be run according to the ICH Q5A8 guideline; to this instance, SSMs may turn useful to validate the process at the laboratory scale. Other points to be kept in mind refer to the possibility of different layouts, mode of operation, geometry or materials for the systems used in small-scale vs large-scale plants.

Validation and qualification of the SSMs
A risk-based assessment of the parameters of choice can be used to validate the representativeness of model, with key performance indicators (e.g., titer, VCD, etc.) and product quality attributes (PQAs) used to run the comparison. A risk-based approach should be the choice also for the design of the small-scale model, taking into consideration both technical and business risks.
More than just one large-stage run (with a minimum of 3) is suggested to support the full qualification of the small-scale models by statistical analysis, according the survey. The choice to assess or qualify the SSM depends on its intended use.
The dimensions of the model can vary according to its specific target use. A benchtop-scale (1 L to 10 L) is common for upstream unit operations, but micro-scale bioreactors (15 to 250 mL) and pilot-scale (50 to 200L) models are other useful options. The benchtop scale of a chromatography
column can be used to model downstream processes, with micro-scale models or pilot plants as other alternatives. The article also reports a table to help identify the correct choice of the scale-independent “scaling parameter”.

In some instances, it might be advisable to use the same media and buffers as in the real manufacturing process, as well as the same raw materials. Procedures to prepare the buffers and other materials should be also comparable.
The BioPhorum Development Group provided examples of how to address qualification, including a satellite or non-satellite approach for upstream unit operations according to the characteristics of the inoculum transfer and scale of the run, the location of the development laboratories and the commercial site. An important parameter to be considered is the temperature for shipping, should it be required a transfer of materials between different locations; shipping at ≤-65°C is the preferred choice for many companies, writes the authors.
Different procedures for filtration have been also addressed, as well as the analytical setup for small-scale experiments; measures may be run in the QC GMP laboratories associated to the manufacturing site or in non-GMP labs for small-scale model qualification. A mix of the two may represent the preferred option in many cases, indicates the article. Training is fundamental to ensure the consistency of small-scale unit operations independent of the operator. Formal documentation should be also produced should the small-scale model undergo new runs of qualification.

The choice of the statistical methods
All data obtained both from the small-scale model and the large manufacturing plant needs to undergo a statistical analysis to be used for the qualification of the production process.
Descriptive statistical methods may depend upon the satellite or non-satellite character of the study, and they may turn useful to provide data in the form of scattered plots to be used for qualification assessment, for example by SMEs or health authorities.
Inferential statistical methods compare data obtained from the small-scale model and the atscale one, which must be representative of populations and referred to stable processes all over the product lifetime. Attention should be paid to the indication of “equivalent” or “notequivalent” results obtained from the applied method, as errors are possible in the 5-10% of cases.
“This is an important fact often overlooked by scientists and health authorities in evaluating the statistical component in a qualification report. It is also an important rationale for not using statistical methods alone to qualify or not qualify a model”, warn the authors of the article. Possible examples of inferential statistical procedures are the difference tests (or null hypothesis significance tests, NHSTs) known as T-test and F-test. Equivalence tests (Two One Sided T-tests, TOST) are also possible to obtain evidence of equivalency, especially in the case of a satellite design of the experiment. Quality range (QR) methods are another available option, useful to establish the population ranges. Multivariate analysis (MVA) provides the possibility to consider different, time-based data sets simultaneously, thus supporting the study of the processes under a time evolution perspective.