Pharmaceutical Technology Is Now PharmTech: A Sharper Name for a Smarter Industry

Navigating FDA Shakeups and Obesity Drug Wars

Behind the Headlines Episode 28

Ask the Expert

Closing the Gap in Modern Process Control and Automation for QC/QA

Drug Digest December 2025

How It Helps Avoid Facility and Equipment Problems

A “Plan, Prevent, Prove” Approach

Felicity Thomas is the former Associate Editorial Director of Pharmaceutical Technology, Pharmaceutical Technology Europe, BioPharm International.

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Ensuring drugs are safe when administered is of utmost importance to the pharma industry. For inhalation drugs, there are strict regulations that must be followed to ensure the drug can be inhaled safely and without any negative or damaging effects. Currently, animal models are commonly used for the safety assessments of inhalation drugs; however, such models are not accurate at predicting how the drug will interact in a human. Another technique for safety assessment is the use of human cells that are taken by lung scraping, which is an invasive process and only provides tissue from the upper lung.

To overcome the limitations of the current approaches, a three-dimensional (3D) human lung model has been developed. The approach, which was originally developed by Abigail Martin while she was working on her PhD research at the University of Hertfordshire, involves the growth of human lower lung tissue and immune cells in a laboratory so that a 3D model can be created. The model can then be used in the place of animal testing and allow companies to test the impact of their drug product on the small airways and inflammation in the lung in a reproducible and more reliable way.

“Having worked on drug development projects that involve animal testing, I’ve seen the difference that new drugs can make to patients’ lives. However, I felt there had to be a better way and set out to look for an alternative,” explained Martin in a press release (1). “Our model not only enables companies to move away from animal testing but also improves safety for patients too. We look forward to working with many more clients and scaling the business with a view to global expansion in the future.”

After having developed the model, Martin collaborated with her PhD supervisor, Victoria Hutter, to set up the company ImmuONE. In efforts to scale up and expand their operations, Martin and Hutter have managed to raise £2 million in investment, securing backing from the MEIF Proof of Concept and Early Stage Fund, which is managed by Mercia Ventures and part of the Midlands Engine Investment Fund.

Hopefully, with this novel approach for safety assessments in inhalation drugs gaining traction, industry can move closer to a future where animal testing is avoided completely for drug products.

ImmunONE. Scientists Raise £2M for 3D Human Lung Model to Replace Animal Testing. ImmunONE.com, News Release, 26 Oct. 2023.

When referring to this article, please cite it as Thomas, F. Breathing Easier. 

Articles

A novel approach for safety assessments in inhalation drugs may avoid animal testing for drug products.

Breathing Easier

Bianca Piachaud-Moustakis is lead writer at Pharmavision, Pharmavision.co.uk.

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The use of artificial intelligence (AI) in the European Union (EU) will be governed by the AI Act, which is set to become the world’s first comprehensive legal framework for AI. The AI Act forms part of the EU’s digital strategy and was originally proposed by the European Commission (EC) in April 2021. The general approach policy was later adopted by the European Council in 2022, with the European Parliament most recently adopting its position in mid-June 2023. Following these developments, the three bodies will now negotiate the final details before the policy can become law, in a process called “trilogue,” or a three-way negotiation (1).

The AI Act aims to establish harmonized rules for the development, placing on the market, and use of AI in the EU, and its principal aim is to turn the EU into a global trustworthy hub for AI. The scope of the AI Act is very wide, covering systems developed through an array of approaches that are listed in Annex I of the AI Act. These include machine learning approaches that also incorporate deep learning; logic and knowledge-based approaches; as well as statistical approaches, Bayesian estimation, search, and optimization methods (2).

The European Parliament’s priority is “to make sure that AI systems used in the EU are safe, transparent, traceable, prevents bias and discrimination, fosters social and environmental responsibility, and ensures respect for fundamental rights” (3). Crucially, the European Parliament believes that AI systems should be overseen by people, rather than by automation, to prevent harmful outcomes (4).

The parliament also aims to establish a technology-neutral, uniform definition for AI that could be applied to future AI systems. To this end, AI is defined as “software that is developed with one or more of the techniques and approaches listed in Annex I and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with” (1). Interestingly, this definition of AI is focused on its outputs and objectives, rather than its underlying technology or algorithms because the regulation aims to establish a framework for the ethical and trustworthy development and use of AI systems in the EU (5).

As regards intellectual property (IP) rights, the European Parliament has also stressed the importance of addressing issues relating to patents and new creative processes, as well as resolving questions of ownership relating to something that is entirely developed by AI (3).

The new rules follow a risk-based approach so that AI systems can be effectively assessed. Through this methodology, the European Parliament establishes obligations for providers and users depending on the level of risk that AI can generate. Providers are those who “develop an AI system to place it on to the market or put it into service under their own name or trademark” (6). The Act splits AI into the following four bands of risk based on the intended use of a system:

 Under the new proposals, AI systems with an unacceptable level of risk to people’s safety would be strictly prohibited. These include systems that deploy subliminal or purposefully manipulative techniques (such as cognitive behavioural manipulation) to exploit people’s vulnerabilities; systems that are used for social scoring (by classifying people based on their social behaviour, socio-economic status, and personal characteristics); and the use of real-time and remote biometric identification systems, such as facial recognition (7).

 High-risk AI systems are subject to a detailed certification regime but are not deemed so fundamentally objectionable that they should be banned. These systems are divided into two categories:

AI systems that are used in products that come under the EU’s General Product Safety (8) legislation that includes toys, aviation, cars, medical devices, and lifts 

AI systems that fall into eight specific areas that will have to be registered in an EU database:

biometric identification and categorization of natural persons

management and operation of critical infrastructure

employment, worker management, and access to self-employment

access to and enjoyment of essential private services and public services and benefits

migration, asylum, and border control management

assistance in legal interpretation and application of the law (4).

All systems deemed ‘high risk’ will be assessed before being placed on the market and throughout their lifecycle.

 Limited risk AI systems are required to comply with minimal transparency requirements that would allow users to make informed decisions. This category includes AI systems such as chatbots, emotion recognition and biometric categorization systems, and systems generating ‘deepfake’ or synthetic content (6). The legislation stipulates
that users should be made aware when they are interacting with AI and be given an option whether they want to continue using it.

. Minimal risk includes applications such as spam filters or AI-enabled video games for which the commission proposes regulation primarily by voluntary codes of conduct.

The AI Act will also establish a European Artificial Intelligence Board, which will be responsible for overseeing the implementation of the regulation and ensuring uniform application across the EU. The body will be tasked with putting forward recommendations on issues that arise as well as providing guidance to national authorities. According to the legislation, the board should reflect the various interests of the AI ecosystem and be composed of representatives of the EU member states (9).

Implications for the health technology sector

According to Burges Salmon (10), the AI Act is intended to directly affect health technology companies whose AI systems are placed on the EU market, and are subject to third-party conformity assessments (made by notified bodies), as stipulated by the EU’s Medical Devices Regulation (MDR), [Regulation (EU) 2017/745] (11) and 

 Diagnostic Regulation (IVDR), [Regulation (EU) 2017/746] (12). These systems include AI-enabled diagnostic tools, therapeutic devices, or implantable devices such as pacemakers. Furthermore, the AI Act will also affect MedTech companies whose AI systems are not directly affected by the proposed legislation, including AI-enabled general practitioner (GP) apps, patient chatbots, and fall detection systems (assuming that these systems are not already subject to the MDR or IVDR regulations).

For health technology companies whose products fall under the category of ‘High Risk’ AI systems, they will have to meet significant obligations in relation to:

Although the safety risks specific to AI systems are meant to be covered by the AI Act, the overall safety of the product, and how the AI system is integrated, will be addressed by the conformity assessment under the MDR or IVDR regulations, given that the AI Act is intended to “be integrated into the existing sectoral safety legislation [including the MDR and IVDR] to ensure consistency, avoid duplications, and minimize additional burdens” (10).

On 14 June 2023, Members of the European Parliament adopted a negotiating position on the AI Act, with talks now taking place with EU countries in the council regarding the final form of the law. The aim is to reach an agreement by the end of this year, and it is possible that the new legislation could enter into force in 2023. The majority of the provisions will then apply another 24 months later, during which time companies and organizations will have to ensure that their AI systems comply with the requirements and obligations set out in the regulation.

Lynch, S. Analysing the European Union AI Act: What Works, What Needs Improvement. Human-Centred Artificial Intelligence (HAI) Stanford University, hai.stanford.edu, 21 Jul. 2023.

Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts.

AI Rules: What the European Parliament Wants.

EU AI Act: First Regulation on Artificial Intelligence.

Taylor Wessing. Introduction on the AI Act of the European Union. Insights (accessed 10 Oct. 2023).

The EU AI Act: A Summary of its Significance and Scope

AI Act: A Step Closer to the First Rules on Artificial Intelligence

Directive 2001/95/EC of the European Parliament and of the Council on General Product Safety

MacCarthy, M.; Propp, K. Machines Learn that Brussels Writes the Rules: The EU’s New AI Regulation. Brookings Institution, Commentary, 4 May 2021.

Whittaker, T.; Slocock, A. The EU’s Proposed AI Act: A Quick Guide for Health Tech. Burges Salmon, Press Release, 25 Jul. 2022.

Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on Medical Devices, Amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and Repealing Council Directives 90/385/EEC and 93/42/EEC (Text with EEA relevance).

 Current consolidated version: 20 Mar. 2023.

Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on In Vitro Diagnostic Medical Devices and Repealing Directive 98/79/EC and Commission Decision 2010/227/EU.

Bianca Piachaud-Moustakis is lead writer at PharmaVision, Pharmavision.co.uk.


Vol. 35, No. 11
November 2023
Pages: 8–9

When referring to this article, please cite it as Piachaud-Moustakis, B. The EU AI Act. 

The EU’s AI Act is set to become the world’s first comprehensive legal framework for artificial intelligence.

The EU AI Act

Matthew Hewitt is vice-president, technical officer, Cell & Gene Therapy & Biologics, at Charles River Laboratories.

Larry Bellot, PhD, is scientific advisor, Cell and Gene Therapy, at Charles River Laboratories.

Chad Andersen is associate director, Manufacturing Sciences & Technology, at Charles River Laboratories.

Cell and gene therapies (CGTs) are reshaping multiple therapeutic areas, including oncology, rare diseases, bleeding disorders, and monogenetic disorders, giving patients new, potentially curative treatment options. The first commercial cell and gene therapies were approved in 2017. Through the end of 2022, there were 12 approved cell and gene therapies (CGTs) in the United States. So far in 2023, there have been five cell and gene therapy US approvals, another three have FDA decision dates in 2023 (1). The field continues to advance rapidly, and, at last count, there are nearly 1700 active CGT clinical trials worldwide (1). In the early days of CGT, there was a saying frequently spoken which has become a mantra for the field: “the process is the product,” meaning there is some tribal knowledge associated with manufacturing these therapies. This mantra can be detrimental to scaling manufacturing to meet the demand of larger patient populations. Since this mantra relies on specific operators having specialized tribal process knowledge, manufacturing processes can be difficult to transfer to other facilities.

Strengthening manufacturing processes and analytics are a priority and the field has begun to align on removing nuance as well as tribal knowledge from manufacturing in an effort to close, automate, and ensure processes are transferrable. The bedrock of this process is an efficient and effective technology transfer (tech transfer) process. A well-defined and robust technology transfer process involves a systematic approach to transferring manufacturing processes, protocols, and knowledge from the research and development phase to the clinical and potentially commercial manufacturing setting, as shown in 

. This process ensures the developed cell and/or gene therapy can be produced consistently, efficiently, and in compliance with regulatory requirements. The same requirements exist for transferring analytical methods to contract development and manufacturing organizations (CDMOs) or equivalent. 

 Technology transfer flowchart from development into good manufacturing practice (GMP) execution. NPI is new product information.

How does one make a peanut butter and jelly sandwich? It is a simple enough question, but one might find that if a person follows those instructions literally, it is quite difficult to execute without a large amount of detail. This is what CDMOs within the CGT field confront frequently. CDMOs take, at times, manufacturing processes quite early in development and transform them into processes suitable for certified good manufacturing practices (CGMP) manufacturing. While true, to a degree CGT drug products must adhere to pre-defined specifications much like other drug products administered to patients. This article will describe the necessary steps and strategies, as shown in Figure 1, to transfer a R&D manufacturing and analytical suite to GMP for clinical and commercial manufacturing. As the authors describe this journey, they will describe the different elements of the journey, such as establishing critical quality attributes (CQAs), critical process parameters (CPP), and target product profile (TPP). The authors will also offer some commentary on where they believe CGT manufacturing is heading in the near, mid-term, and long-term future.

The planning stage is the critical first step in ensuring the success of a tech transfer into the manufacturing facility. This phase involves careful evaluation, strategic decision making, and comprehensive planning to lay the foundation for a smooth and effective transfer.

Risk assessments and mitigations play a crucial role during this phase by ensuring the success of the tech transfer. They help to identify potential challenges, uncertainties, and vulnerabilities that could impact the transfer process or the quality of the final product. By proactively addressing these risks and implementing mitigation strategies during the initial assessment and planning stage, organizations can enhance the efficiency, reliability, and safety of the transfers.

By conducting a thorough and strategic initial assessment and planning stage, organizations can set the stage for a successful tech transfer. This phase provides a solid framework for subsequent steps, ensuring that the transfer process is well-informed, well prepared, and positioned for success.

Knowledge transfer is the second and most involved step of a successful tech transfer. It involves the effective dissemination of expertise, protocols, and insights from the research phase to the manufacturing setting. Cross-functional collaboration between research, manufacturing, and quality assurance facilitates a smooth exchange of knowledge. Clear and comprehensive documentation, including detailed standard operating procedures (SOPs) and process descriptions, captures critical information for seamless replication.

A continuous feedback loop also encourages ongoing learning, incorporating insights from manufacturing teams into refining protocols. Regulatory alignment ensures compliance and adherence to guidelines. In essence, successful knowledge transfer ensures that the innovation harnessed in research is translated effectively, leading to consistent, high-quality outcomes in CGT manufacturing and accelerating the availability of transformative treatments to patients in need.

Training during tech transfer is critical in ensuring a seamless transition from research to practical applications, such as cell therapy. Structured training programs provide personnel with the essential knowledge and skills needed to effectively execute transferred processes. These programs encompass both theoretical understanding and hands-on experience, enabling individuals to grasp the intricacies of manufacturing protocols. Mentorship and shadowing opportunities offer direct exposure to the procedures, fostering a deeper understanding of practical implementation. Continual learning is encouraged through resources such as workshops, webinars and online courses, allowing teams to stay updated on best practices. The benefits of comprehensive training extend beyond initial implementation, as well-trained personnel contribute to enhanced process efficiency, minimized errors, and, ultimately, the consistent production of safe and effective cell therapies. Through training, technology transfer becomes a catalyst for knowledge acquisition and competence development, enabling organizations to fully leverage their scientific advancements for real-world impact.

Concurrent with the tech transfer of the manufacturing process is the transfer of the methods for assessing the CQAs required to release the final drug product. Each product is unique and complex. As such, method development of the requisite set of assays is typically necessary. Depending on the stage of the developer, analytical development can range from establishing new methods, to simply updating methods, to compliant instrumentation.

For early phase developers, assay development for release of final product is dependent on process development completion to obtain “final product” material for testing. This is often the case as early phase developers have not established their final product formulation, and do not have formulated material on hand for analytical development. For example, in the case of cell therapy identity assays, which are mainly performed via flow cytometry, this gap can be mitigated with the use of “like” final product materials in the formulated state of the final product for these early phase developers. Similarly, once the formulation is set, some purity assays can be developed and assessed by spiking in the potential impurities into the final product formulation buffer to determine whether the assay is fit for the intended purpose.

One important consideration to note in assay development is that potency assays do require final formulated product. With an orthogonal (matrixed) approach to early potency assay development, substantial material may be required. Many developers do not consider the amount of material required for proper assay development, so this point is critical for developers to understand. As a result of this oversight, assay development of potency assays often lags slightly behind process development during a transfer for the early phase developer.

Another area that is often overlooked during method development is establishing the appropriate assay control(s). Ideally, an assay control would be able to span multiple assays in the assessment suite. Method controls can be either a “reference material”, which are assay controls established from lots of manufactured final product material, or “control material”, that is, mass produced to meet exact specification ranges but not necessarily all the characteristics specified by the assay. As is the case with material needed for general development, early phase developers may not have stores of reference material to add to their assays. If reference material is available, developers generally prefer to dedicate such material for use in optimizing key release assays. For some early phase developers, off-the-shelf cellular kits or control materials have, and can be, used as assay performance controls provided these controls meet assay requirements.

Once the analytical method is shown to be fit for its intended purpose, the locked method is ready to be transferred from development to a routine testing lab in quality control (QC). Prior to initiating the transfer of the method, the transferring lab plans the tech transfer of the method. The transferring lab ensues that proper training of the analysts is provided, the materials are added to the bill of materials for use for this assay, and the necessary instruments and software are installed in the receiving lab.

Tech transfer of the method to the receiving lab will be considered complete once the receiving lab has successfully executed the validated method. The scope of the validation will be dependent on the stage of the developer, whether the method in question will be used for releasing product or for characterization of the product, and the method transfer strategy employed.

The validation model could employ a comparative testing strategy, a co-validation strategy, partial validation, or complete validation. Phase-appropriate stringency should be applied to the validation parameters assessed, given the characterization status of the method being transferred. While often overlooked for early phase developers, it is recommended that certain robustness measures be performed to provide validation data to support impact statements which may arise from potential perturbations that can be expected in the QC laboratory. The overall success of the tech transfer plan determines the method robustness needed to produce CQA analytics during early-phase and pivotal clinical trials by gathering analytical performance data.

As the CGT field continues to mature, a well-defined technology transfer process is critical to ensure manufacturing therapeutic consistency. A defined tech transfer process for both manufacturing and analytics can ensure that batch production records are properly constructed and the analytics are phase-appropriately qualified and validated. The result is a well-defined manufacturing process and analytical suite which should be transferrable to any other facility.

While other challenges continue, a proper technical transfer is key for CGTs to reach their full therapeutic potential. The field must find avenues to efficiently scale (up and out) manufacturing to meet the needs of larger patients populations. This is critical for patients with limited treatment options.

As such, organizations should not work as competitors in this area, but, rather, as collaborators much like the industry did in the early days of monoclonal antibodies.

1. Alliance for Regenerative Medicine, The Sector Snapshot: August 2023. 

alliancerm.org/the-sector-snapshot-august-2023

Matthew Hewitt is vice-president, technical officer, Cell & Gene Therapy & Biologics; Larry Bellot, PhD, is scientific advisor, Cell and Gene Therapy; and Chad Andersen is associate director, Manufacturing Sciences & Technology; all at Charles River Laboratories.


Volume 47, No.11
November 2023
Pages: 39–41

When referring to this article, please cite it as Hewitt, M.; Bellot, L.; Andersen, C. Best Practice Tech Transfer Methods for CGTs. 

A look at using best practice tech transfer methods for CGTs to increase process and analytics robustness while being scalable.

Best Practice Tech Transfer Methods for CGTs

Cheryl Barton is director of PharmaVision, 

iofetamine (123i) molecule, molecular structure, iofetamine, ball and stick 3d model, structural chemical formula with colored atoms | Сергей Шиманович - stock.adobe.com

Ariceum Therapeutics, a private biotechnology company, based in Germany, is focused on developing novel drugs for systemic targeted radiation therapy (STRT) for hard-to-treat cancers. The name Ariceum is an anagram of ‘Marie Curie’ who discovered the elements radium and polonium, which have been hugely influential in the discovery of cancer treatments. The company was co-founded in 2021, and Ipsen transferred assets and all corresponding rights into the company on 22 Oct. 2021. Industry veteran Manfred Rüdiger, PhD, is the CEO of Ariceum, and Sandy McEwan, the company’s chief medical officer, is a world-renowned expert in oncology and nuclear medicine (1).

Over the past decade, significant advances have been made in radiation therapy, particularly in the development of systemic targeted radiopharmaceuticals to reduce potential side effects. Using an artificial intelligence (AI)-enabled platform, Ariceum has focused on designing novel targeting molecules that either bind to receptors on the tumour cell surface or the DNA within the tumour cells. This allows the radioactive payload to accumulate within the tumour and reduces exposure of healthy tissues to radioactivity (2).

Ariceum’s lead candidate in Phase II trials—beta-emitting lutetium-177 radiolabelled satoreotide tetraxetan (SS0110), a somatostatin type 2 (SST2) receptor antagonist—is being developed as a theranostic for the diagnosis and treatment of hard-to-treat cancers. SST2 receptors are overexpressed in many cancers, such as small cell lung cancer (SCLC), high-grade neuroendocrine tumours (NETs), and neuroblastoma, an aggressive, rare type of cancer that occurs mainly in young children (2). Satoreotide binds to the SST2 receptor on the tumour surface to deliver alpha and beta particles into the cancer cell within a radius of 1–10 mm of the receptor binding site.

The company recently published details of a Phase I/II trial, NCT02592707, assessing the safety and efficacy of satoreotide where it was shown to be safe and effective in patients with progressive, SSTR-positive NETs (3,4). A five-year long-term follow-up study is ongoing, and the study is due to be completed on 1 April 2025 (5). The company is also evaluating satoreotide in Phase II for SCLC, Phase I for Merkel cell carcinoma (MCC), and pre-clinical evaluation in solid tumours. In addition, Ariceum is evaluating SS0120, a gamma-ray-emitting Gallium-68 radiolabelled satoreotide trizoxetan as an imaging agent in patients with gastroenteropancreatic neuroendocrine tumours (6).

Ariceum’s second targeting molecule is a Poly (ADP-ribose) polymerase inhibitor (PARPi) which is expressed at a low level in healthy tissues compared to cancer cells (2). ATT001 is an iodine-125 radiolabelled PARP inhibitor (123I PARPi), which targets activated PARP bound to tumour DNA to release radiation in the cell nuclei through the emission of Auger electron (7). It is due to enter Phase I development for the treatment of glioblastoma following promising results in preclinical and first-human studies (8). In addition, ATD001 a fluorine-18 radiolabelled rPARPi is currently in Phase I clinical trials at Memorial Sloan Kettering Cancer Center. The first clinical study with ATD001 in head and neck cancers completed recruitment in May 2019 and trial data was published in 2020 (9).

In June 2023, Ariceum acquired UK-based biotech Theragnostics Ltd for US$44 million (€42 million)—US$2.5 million (€2.4 million) upfront plus cash and milestone payments totaling up to $41.5 million (€39.4 million)—to help expand its radiotherapeutic pipelines. The deal will enable Ariceum to gain access to NEPHROSCAN, a US Food and Drug Administration (FDA)-approved diagnostic developed in partnership with GE Healthcare, and gallium-68 (68Ga) kit technology intellectual property which is currently licensed to Novartis (10).

In June 2023, Ariceum signed a pharmaceutical multi-project agreement with Eurofins contract development and manufacturing organization (CDMO) to secure clinical trial services to support its Phase I/II clinical trial programme in Australia (11). In May 2023, the company signed a strategic research collaboration with UCB to discover new modalities for the treatment of immune-related diseases and cancers (10). UCB will leverage its proprietary messenger RNA-display platform, ExtremeDiversity, to support Ariceum in the discovery of three new peptide-radioisotope conjugates.

In June 2022, it completed a €25 million Series A financing round led by EQT Life Sciences (formerly LSP) HealthCap and Pureos Bioventures, and these funds will be used to advance its lead radiopharmaceutical product, satoreotide (1). In April 2023, Ariceum extended a Series A financing round to raise an additional €2.75 million funds to advance its clinical pipelines. New investors Andera Partners and Earlybird Venture Capital, join existing investor, Pureos Bioventures (12).

There is significant interest in radiotherapeutics, and several companies in Europe and the United States, including France-based Advanced Accelerator Applications (a Novartis Company) and Curium Pharma, and Germany-based ITM Isotope Technologies Munich SE, Germany, and US-based NuView, Telix Pharmaceuticals, RayzeBio, and Y-mAbs.

On 26 Sep. 2023, Genentech teamed up with Japan-based biopharmaceutical company PeptiDream to discover and develop novel peptide-radioisotope drug conjugates (13). According to the transaction terms, PeptiDream will receive an upfront payment of US$40 million (€38 million) from Genentech. PeptiDream will be eligible for potentially up to US$1 billion (€949 million) in payments based on certain development, regulatory, and commercial milestones.

On 3 Oct. 2023, Eli Lilly acquired POINT Biopharma for US$1.4 billion (€1.3 billion) to expand its oncology capabilities and gain access to next-generation radiotherapeutics (14). POINT’s lead programs in late-phase development include:

PNT20021 is a prostate-specific membrane antigen (PSMA) targeted radioligand therapy in development for patients with metastatic castration-resistant prostate cancer (mCRPC) after progression on hormonal treatment.

PNT20031 is an SSTR-targeted radioligand therapy in development for the treatment of patients with gastroenteropancreatic neuroendocrine tumours (GEP-NETs).

On 4 Oct. 2023, Novartis AG announced that it was considering selling part of the radiopharmaceuticals company, Advance Accelerator Applications (AAAs) as part of its business strategy to focus on high-risk, high-reward business units (15). AAAs pipeline includes Lutathera (lutetium-177 oxodotreotide) a first-line therapy against GEP-NETs, Netspot, and SomaKit which use 68Ga labelled somatostatin analogue to target neuroendocrine tumours, Locametz a 68Ga labelled prostate-specific antigen (PSA) and Gluscan, which tracks glucose metabolism with fluoride-18 (16).

Although radiation therapy has been used for years in the treatment of cancer technological radiotherapeutics are relatively new on the scene. Technological advances have improved the targeting of radiation therapy at the cellular level, and reduced side effects. Many of the larger pharma companies including AstraZeneca, Bristol Myers Squibb, Eli Lilly, Merck & Co, and Novartis have invested in this area and several agents have entered late-stage clinical development and/or entered the market. The next-generation radiotherapeutics offer significant potential to revolutionize the treatment of hard-to-treat cancers such as glioblastoma and neuroendocrine tumours, which up until now evaded treatment through traditional methods.

EQT Group. EQT Life Sciences Announces Launch and Series A Financing Round of portfolio company Ariceum Therapeutics. Press Release. 10 Jun. 2023.

Wild, D.; Grønbæk. H.; Navalkissoor, S. et al. A Phase I/II Study of the Safety and Efficacy of [177Lu]Lu-satoreotide Tetraxetan in Advanced Somatostatin Receptor-positive Neuroendocrine Tumors. Eur J Nucl Med Mol Imaging. 2023 Sep 18. doi: 10.1007/s00259-023-06383-1. Epub ahead of print. PMID: 37721581.

An International, Multicenter, Open-label Study to Evaluate Safety, Tolerability, Biodistribution, Dosimetry, and Preliminary Efficacy of 177Lu-OPS201 for the Therapy of Somatostatin Receptor-positive Neuroendocrine Tumors (NETs), NCT02592707. 

A Multicentre Surveillance Study to Evaluate the Long-term Safety in Participants Who Have Been Previously Treated With 177Lu-IPN01072 in an Ipsen-sponsored Clinical Study. NCT05017662. ClinicalTrials.gov (accessed 12 Oct. 2023).

Virgolini, I.; Bahri, S.; Kjaer, A.; et al. A Randomized, Factorial Phase II Study to Determine the Optimal Dosing Regimen for 68Ga-Satoreotide Trizoxetan as an Imaging Agent in Patients with Gastroenteropancreatic Neuroendocrine Tumors. 

, 63 (3), 376–383. doi: 10.2967/jnumed.121.261936. Epub 2021 Jul 2. PMID: 34215673; PMCID: PMC8978200.

Young, R.J.; Demétrio De Souza França P.; Pirovano, G.; et al. Preclinical and First-in-Human-Brain-cancer Applications of [18F]poly (ADP-ribose) Polymerase Inhibitor PET/MR. 

 2020 Sep 15;2(1):vdaa119. doi: 10.1093/noajnl/vdaa119. PMID: 33392502; PMCID: PMC7758909.

Schöder, H.; França, PDS.; Nakajima, R.; et al. Safety and Feasibility of PARP1/2 Imaging with 18F-PARPi in Patients with Head and Neck Cancer. 

, 26 (13), 3110–3116. doi: 10.1158/1078-0432.CCR-19-3484. Epub 2020 Apr 3. PMID: 32245901; PMCID: PMC7421489.

Ariceum Therapeutics. Ariceum Therapeutics Announces Extension of Series A Financing to EUR 47.75M to Advance its Next Generation Radiopharmaceutical Clinical Pipeline. Press Release. 1 Jun. 2023.

Ariceum Therapeutics. Ariceum Therapeutics, and Eurofins CDMO Sign a Pharmaceutical Multi-Project Agree

Ariceum Therapeutics. Andera Partners, and Earlybird Venture Capital Join Existing Investor, Pureos Bioventures. Press Release. 18 Apr. 2023

PeptiDream. PeptiDream Announces Collaboration and License Agreement with Genentech for the Discovery and Development of Novel Peptide-Radioisotope Drug Conjugates. Press Release. 19 Sept.2023.

Eli Lilly. Lilly to Acquire POINT Biopharma to Expand Oncology Capabilities into Next-Generation Radioligand Therapies. Press Release. 3 Oct. 2023.

Novartis. Novartis Executes Sandoz Spin-off, Completing the Strategic Transformation into a Leading, Focused Innovative Medicines Company. Press Release. 4 Oct. 2023.

Bloomberg. Novartis Weighs Selling Assets from Radiopharma Unit. Press Release. 3 Oct. 2023.

Cheryl Barton is director of PharmaVision, info@pharmavision.co.uk.


Vol. 35, No. 11
November 2023
Pages: 13-15

When referring to this article, please cite it as Barton, C. Frontrunners in Next-Generation Radiotherapeutics. 

New radiotherapeutic agents are making headway in hard-to-treat cancers.

Frontrunners in Next-Generation Radiotherapeutics

Communication between the manufacturing plant and retail stores. | Image Credit: © Cagkan - © Cagkan - stock.adobe.com

TriLink BioTechnologies, a Maravai LifeSciences Company, announced on Nov. 2, 2023 that it has launched the Analytical Sciences Center of Excellence (ASCE). The new ASCE hub expands the company’s suite of analytical testing capabilities and, according to the company, will minimize cost and delays associated with outsourcing testing to multiple laboratories. The ASCE, which is located in Maravai LifeSciences’s Pacific Center Boulevard facility in San Diego, Calif., features a 4000-square-foot lab to support plasmid DNA and messenger RNA (mRNA) manufacturing and will be responsible for developing analytical methodologies such as mRNA fingerprinting and sequencing.

“TriLink's new Analytical Sciences Center of Excellence will be a transformative force in the development of nucleic acid therapies,” said Khaled Yamout, senior director of Analytical Services at TriLink, in a press release. “This new lab will allow us to expand our analytical capabilities to meet the growing demands of the market, including specific method development.”

TriLink has developed and qualified 10 unique methods for mRNA characterization with 40 various constructs. The ASCE’s new instrumentation to improve nuclear magnetic resonance, next-generation sequencing, and lipid nanoparticles characterization builds on the company’s method development for construct-specific assays.

“We continue to witness exciting growth in nucleic acid therapeutics—and rapid expansion calls for rapid innovation,” added Kevin Lynch, TriLink’s vice-presdient and general manager of GMP Operations, in the press release. “The ASCE’s centralized analytical capabilities and development of analytical methodologies for the characterization of nucleic acids will advance the industry as we continue to find ways to meet the needs of developers working on life-saving therapeutics.”

TriLink, in addition to the new hub, has an extensive mRNA capping portfolio, advanced scale-up capabilities, and expertise in mRNA, oligonucleotide, and plasmid production. It has been a leader in nucleic acid and mRNA solutions for more than 25 years and provides chemical and biological experiences, contract and development manufacturing services, and readymade and custom materials.

The new centralized hub will provide advanced testing of nucleic acids, which is expected to simplify mRNA substance testing.

TriLink BioTechnologies Launches Analytical Sciences Center of Excellence

Feliza Mirasol is the science editor for 

On Oct. 30, 2023, Aragen, a contract research development and manufacturing organization (CRDMO), announced that it was setting up a new biologics manufacturing facility in Bangalore, India as part of a $30 million investment.

The new 160,000 m2 facility will include process development labs and multiple good manufacturing practice (GMP) suites as well as supporting functions such as quality control labs (analytical and microbiology). The GMP manufacturing suites will be equipped to conduct intensified bioprocessing using single-use bioreactors and will have advanced downstream purification capability, according to a company press release.

The facility will be designed to offer integrated solutions across the biomanufacturing lifecycle—from process development to process validation, analytical development, pilot production, large-scale drug substance manufacturing, and stability services. The facility will also be equipped to develop and manufacture monoclonal antibodies, therapeutic proteins, and fusion proteins to augment the company’s bioproduction capability in California.

The process development laboratory is slated to be operational by the third quarter of 2024. The first manufacturing suite will open in the third quarter of 2025. The facility will include plasmid DNA, messenger RNA, cell and gene therapy, microbial manufacturing, and additional GMP manufacturing suites in the future to meet demand.

“With the opening of our bio-manufacturing facility, we will empower our customers with even faster development timelines and support their full lifecycle journey from clone development to cell culture manufacturing …, ” said Subodh Deshmukh, CEO, Aragen Bioscience, in the press release.

“Setting up of a biologics manufacturing facility in India is part of our forward integration strategy for our US-based biologics business, allowing Aragen to offer integrated gene-to-GMP solutions to our customers. Once this facility is commissioned, Aragen will offer a unique value proposition to our biologics customers where the early stage discovery will be executed in our R&D labs in California … As the molecule advances, Aragen can tech-transfer the program to our … manufacturing facility in Bangalore, offering better COGS [cost of goods and services] to our customers,” said Manni Kantipudi, CEO, Aragen Life Sciences, in the press release.

As part of a $30 million investment, Aragen is setting up a new biologics manufacturing facility in Bangalore, India.

Aragen to Establish $30 Million Biomanufacturing Facility in India

Chris Spivey is director of Industry Relations and Strategic Partnership, MJH Life Sciences.

Visual inspection is an integral process in the production of filled and sealed pharmaceutical products to ensure there are no particles or contaminants in the finished parenteral dosage form. Currently, both manual and automated processes are used for visual inspection; however, automated systems can be expensive to implement and not always accessible for smaller companies dealing with smaller batch sizes.

Accessibility of automated visual inspection solutions is a key focus for Luo Automation, a Dutch company researching, developing, and producing automated visual inspection machines. To learn more about the company and how it is making visual inspection more accessible, 

 spoke with the company’s founder, Joren van der Horst, at CPHI Barcelona 2023.

"We make visual inspection [solutions] for very small batches,” explained Horst. As with all visual inspection technologies, the primary aim is to check the quality of the medicine and make sure there are no foreign particles or contaminants present, he added.

To perform the inspection, the finished dosage form—syringes, ampoules, and vials—are swirled and then any movement of particles within can be detected, Horst continued. “Another cool thing is the [current interest] in artificial intelligence and machine learning,” he said. “We use machine learning algorithms that are pretty new to also detect static defects. So, not only particles but also leakages and such.”

For Horst, a major differentiator for Luo Automation within industry is the focus on smaller-scale inspection. “If you want, you can buy a huge inspection machine that will cost you a couple of million, but then you need to produce 100 million containers per year,” he stressed. “But, if you have clinical trials or personalized medicine, you cannot [perform the inspection] with a huge machine.”

Luo Automation was featured at the inaugural Start-Up Market on the show floor at CPHI Barcelona 2023. CPHI Barcelona ran from Oct. 24–26, 2023.

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Pharmaceutical Technology Europe® spoke with Joren van der Horst from Luo Automation, a Dutch company focused on automated visual inspection solutions, at the inaugural Start-Up Market on the show floor at CPHI Barcelona 2023.

Making Visual Inspection Accessible with Luo Automation at CPHI Barcelona

Within the highly regulated pharma industry, adoption of new technologies is generally slower than in other industries, as such, despite three-dimensional (3D) printing being available for some time now, its use in the production of medicines is still very much a novelty. However, 3D printing technology has a great potential within the pharma industry, particularly for personalized medicines.

FABRX, a small start-up company that was created by academics from the University College London in the United Kingdom, is focused on the use of 3D technologies in the future of medicine. At CPHI Barcelona 2023, 

 spoke with Carlos Rial Calvo, lead software engineer of FABRX AI—a subsidiary of FABRX based in Spain and started in 2023—to learn more.

“Our aim is to personalize medicine for patients,” explained Rial Calvo. “This is very useful, for example, for highly potent drugs that require very specific dosing.” Additionally, using 3D printing technology to personalize medicine is very useful for pediatrics, who can find swallowing medication difficult, so, Rial Calvo pointed out that it is possible to produce medication that is easier for children to adhere to.

Similarly, for patients that need to take multiple tablets, Rial Calvo noted that with the 3D technology it is possible to create polypills—a combination of different APIs in one printlet (3D printed tablet). “We create polypills, and then [the] patient only has to take one tablet,” he said.

FABRX is currently collaborating with hospitals across Europe, Rial Calvo specified. “For example, we are running a clinical trial in an [oncology] hospital in France to combine an anti-cancer drug with a drug for the side effects [of the cancer treatment] into the same pill,” he stated (1).

In another clinical study, the company has worked with the University of Santiago de Compostela and the University Hospital in Santiago de Compostela in Spain to develop a 3D printed medicine for a rare disease, Rial Calvo revealed. For this study, FABRX prepared doses of isoleucine for children with maple syrup urine disease—an inherited metabolic disorder (2). Using the 3D printing technology, flexible production of a personalized dosage form was possible.

“We provide the software for controlling the machines, but also the software provides features to help with the development of new formulations,” Rial Calvo concluded. “So, the researchers can use our software as well, with the machine, to test new materials.”

1. FABRX. FabRx and Gustave Roussy Enter into an Agreement to Develop a Novel, Personalised, Multi-Drug Dosage Form for the Treatment of Patients with Early-Stage Breast Cancer. 

, Press Release, June 25, 2021.
2. FABRX. FabRx Performs the First Clinical Study Using Personalised 3D Printed Dosage Forms to Treat Children with a Rare Metabolic Disease. 

Fabrx was featured at the inaugural Start-Up Market on the show floor at CPHI Barcelona 2023. CPHI Barcelona ran from Oct. 24–26, 2023.

Pharmaceutical Technology Europe® spoke with Carlos Rial Calvo from FABRX, a pharmaceutical 3D printing company, at the inaugural Start-Up Market on the show floor at CPHI Barcelona 2023.

3D Printing Medicines with FABRX at CPHI Barcelona

H. Gregg Claycamp, MS, PhD, is a Risk and Decision Analysis Consultant, hgclaycamp@gmail.com.

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This paper updates “Expert Judgments in Quality Risk Management: Where Quality Risk Management can ‘Go Wrong’” (1), in which several authors writing on the first 10 years of the International Council for Harmonisation’s (ICH’s) Q9 discussed controlling “subjectivity” in quality risk management (QRM) as a persistent challenge to ICH Q9 implementation. Indeed, “[t]he robustness and validity of a risk management program depend in part on recognition and control of the subjectivity that might be introduced by the many expert-based judgments for decisions that occur during the development and use of risk tools” (1). The revised guideline, ICH Q9 (R1), provides a new section of specific comments on subjectivity, “Managing and Minimizing Subjectivity (5.3),” adjacent to an expanded discussion about the meaning of formality in QRM (2). This paper discusses some of the approaches for controlling subjectivity using elicitation of expert knowledge and probabilities.

This article was peer-reviewed by a member of

The general means for controlling subjectivity in QRM and for risk-based decision making (RBDM) include:

raising awareness about subjectivity through training on probability and expert judgments of subjective probabilities, Heuristics and
biases, and possible impact of subjectivity on RBDM

using formal calibration of individual experts using Cooke’s classical method or other frameworks to elicit subjective probabilities using seed questions for expert calibration, elicit subjective probabilities for the 

 situation, and use formal scoring rules to weight experts’ subjective judgments using subjective probability elicitations in group settings with facilitation, such as Delphi technique, value-focused thinking, and multi-criteria decision making (MCDM) processes.

The first approaches for controlling subjectivity were discussed in the prior paper on ICH Q9 (1). In this paper, the last two approaches are visited, while recognizing that this list is certainly not an exhaustive summary of the many decades of research and application of methods for controlling subjectivity in risk analysis and decision-making under uncertainty.

RBDM often relies on experts for qualitative knowledge about likely sources, predictive factors (parameters), appropriate models, and approaches for controlling identified risks. For complex and significant risks to the patient, RBDM also calls upon experts “to provide quantitative probabilistic assessments of key uncertainties in an analysis when empirical data are unavailable, incomplete, uninformative, or conflicting” (3). This general view of expert elicitation is discussed in the following paragraphs, especially in the context that “[f]ormality in quality risk management is not a binary concept (i.e., formal/informal); varying degrees of formality may be applied […] when making risk-based decisions (3) at §5.1).” In other words, there is no distinct boundary between when qualitative or quantitative elicitations of expert knowledge and probabilities will suffice for a given risk-based decision.

Elicitation of expert knowledge and probabilities

Expert elicitation methods are used as a means of controlling subjectivity and thereby help reduce uncertainty arising in risk management decision-making. For this paper, “expert elicitation” (EE) is used as a general term for processes ranging from a relatively informal and qualitative elicitation of the structure of a risk problem to mathematically formal expert elicitation of risk factors (e.g., variables and parameters), their possible values, and the uncertainties about those estimated values. The latter, known as structured expert judgment (SEJ), is nearly a discipline itself attributed to Roger Cooke and a long list of his colleagues and research collaborators (3–6). SEJ processes apply scientifically justified methods for eliciting experts’ quantitative knowledge to fill critical information gaps in the risk assessment and risk control evaluations.

 The first concept in this use of EE is the qualitative (and possibly informal) elicitation of expert knowledge to help identify decision-making objectives, values, model structure, key risk factors/parameters, and appropriate QRM tools and controls. Here, the SEJ literature is relatively silent on identifying or defining suitable elicitation processes (7).Therefore, the author relies on the volumes of scholarly work about eliciting complex decision-making hierarchies or models for risk-based decisions that are available in the MCDM literature (8–11)(12–14). The benefits of multi-criteria decision analysis (MCDA) for controlling subjectivity in QRM risk modeling and decision-making were previously reported (15). The distinction between a model structure for a quantitative risk analysis and that of MCDA is not trivial. The former seeks a testable mathematical formulation of a risk question while the latter models objectives (goals) performance measured over the possible alternatives (e.g., risk projects). Overall, either approach can inform risk-based decision making.The typical steps in MCDM—which includes MCDA and other decision tools—to explain the two major types of expert elicitation for developing risk models that inform RBDM are given in 

Table I. Typical application of structured expert judgement (SEJ) tools in typical risk-based and multi-criteria decision making (MCDM).

Defining and structuring of a complex risk-based decision begins by eliciting a clear definition of the risk in question and the principal factors contributing to the risk (

). The hierarchical view of a complex multi-objective or multi-criteria decision problem is sometimes referred to as an “objectives hierarchy” acknowledging that the purpose of a decision is to achieve one or more specific objectives (12,16,17). In contrast, the purpose in most classical expert elicitations is to have expert knowledge stand in for data, risk parameters, and the uncertainty in the parameters. The elicited data distributions and parameters are then used to calculate risk. Although the theoretical underpinnings differ, a decision objective to “reduce risk to the patient” can lead to similar results as the predictive endpoint of a quantitative, expert-built risk model. Most importantly, both processes inform decision-making about the need to control risk.

 shows a small section of an elicited objectives hierarchy model that might occur in prioritizing pharmaceutical manufacturing sites for good manufacturing practice (GMP) surveillance inspection. In this view, the model includes inherent product and process factors, among others, that experts identify as contributing to overall risk. The attributes of these factors are measurable parameters that combine (usually linearly) to estimate a defined risk value or a score, “upwards” in the hierarchy.A relevant example of qualitative expert knowledge elicitation is found in the development of FDA’s original “GMP Site Selection Model” for GMP surveillance inspections. The model development began in 2003 using qualitative expert elicitation of the key parameters that might predict risk based on methods in the MCDM literature (18). The important attributes that experts believe contribute to a “risk score” for the purpose of GMP surveillance inspections were elicited through both focus group and survey methods of approximately 80 pharmaceutical manufacturing and compliance experts. Since 2005, the FDA site-selection model has undergone many iterations, continuous improvement, and peer-review; however, many of the original risk factors and attributes are retained (19).

Figure 1. Hypothetical and partial elicited hierarchy for a good manufacturing practice (GMP) surveillance model. Elicitation of expert knowledge about the factors important in predicting risk can be organized hierarchically as an aid to risk model communication. The hierarchies range from a handful of criteria to a complex hierarchy of (e.g.) 60 criteria and attributes the author elicited from experts in adverse drug experience reporting. (Figure courtesy of the author)

Expert elicitation for structured expert judgments. A formal quantitative elicitation of expert judgments of probabilities, such as described in Cooke’s classical method for SEJ (20), is the second concept within expert elicitation as defined herein. Rigorous SEJ is used in high-impact RBDM where eliciting continuous risk estimates and the cumulative distribution—a presentation of uncertainty in the risk estimate—may be crucial. The steps for eliciting experts for SEJ are shown in 

 illustrates using theoretical probability densities—the notion that the expert is elicited to obtain a justifiable estimate of likely value and uncertainty for a risk parameter to use in making risk estimates. 

 illustrates the simplest form of eliciting subjective probabilities, which is a common starting point for novices in the practice. This method is sometimes used to initialize parameters for monte carlo risk models. But SEJ is much more complicated, as outlined in 

. The process steps given in Table II mask the very complicated process of eliciting judgments using interviews of one expert at a time before aggregating experts’ results for modeling. Unfortunately, the one-on-one is notorious for being time consuming and challenging for the experts and facilitators alike.

Table II. Structured expert judgment. After: Cooke (5), Kurowicka and Cooke (6). 

Cooke’s classical model relies significantly on calibrating experts by asking them to estimate value and probability distributions of approximately 10 quantities that are reasonably knowable in their discipline. The results of estimating probabilities in the so-called “seed questions” exercise is used under proper statistical scoring rules to calculate two scores for each expert. The first is a calibration score that roughly measures accuracy using statistical distance measures from the known value of the parameter. The second is an information score measuring the match of the expert’s probability distribution and the known parameter distribution (21). Placing estimates in probability bins or probabilities on values estimates can be used (

More attention should be given to how expert opinions and judgments are elicited for reducing uncertainty in quality risk management and risk-based decision making. 

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