generative AI Archives - European Industrial Pharmacists Group (EIPG)

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

Environmental sustainability: the EIPG perspective


Piero Iamartino Although the impact of medicines on the environment has been highlighted since the 70s of the last century with the emergence of the first reports of pollution in surface waters, it is only since the beginning of the Read more

How AI is Changing the Pharma Industry and the Industrial Pharmacist’s Role

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Svala Anni, Favard Théo, O´Grady David

The pharmaceutical sector is experiencing a major transformation, propelled by groundbreaking drug discoveries and advanced technology. As development costs in the pharmaceutical industry exceed $100 billion in the U.S. in 2022, there is a pressing need for innovative solutions to accelerate drug development. The urgency stems from a renewed focus on novel approaches, driven by the complexities of advanced therapeutic modalities like mRNA, CGT, and synthorins. This blog delves into the influence of Artificial Intelligence (AI) on overcoming the unique hurdles within the manufacturing domain of the pharmaceutical industry. It specifically emphasizes the crucial partnership between AI and human expertise, shedding light on the vital role of industrial pharmacists in optimizing manufacturing processes.

The demands for precision, quality, and compliance in pharmaceutical manufacturing present challenges, notably in managing rising costs and intricate logistical processes. The adoption of various AI technologies, including generative AI (GenAI), represents a strategic shift, aiming to augment human capabilities while automating routine tasks and facilitating knowledge transfer in the ever-evolving landscape of pharmaceutical production

The fourth industrial revolution is upon us with the development of cyber physical systems and the fifth industrial revolution is on the horizon with the advancement of artificial intelligence (AI) in partnership with humans to enhance workplace processes. The factory of the future is here with digitalization, AI, Big data, robotics and advanced manufacturing becoming the norm rather than the exception in the pharmaceutical industry.

GenAI excels in promoting collaboration while surpassing traditional task automation. It is key in transmitting complex knowledge crucial for maintaining quality, compliance, and safety in pharmaceutical manufacturing. AI empowers experts to document processes using everyday devices, transforming this raw data into straightforward, visual instructional guides.

The pharmaceutical industry confronts distinct manufacturing challenges, including complex processes and rigorous regulatory standards. AI can offer several innovative and compliant solutions. In addition, AI platforms swiftly update training materials, creating dynamic learning environments that keep the workforce informed about the latest developments. These platforms are redefining roles by taking over mundane tasks, thereby freeing human workers to focus on more strategic and creative roles. Furthermore, AI guarantees uniform training across global operations, ensuring consistent processes and fostering global standardization.

Combining humans and AI creates a powerful team that may benefit everyone. AI helps make learning experiences unique for each person, fitting their own way of learning. It also makes it easy for people to get the information they need anytime, thanks to the latest tech advancements. AI is great at helping people from different cultures and who speak different languages work together better. It can give feedback right away, so mistakes don’t spread. Plus, AI holds onto valuable knowledge, reducing the chance of losing important information when people leave or retire. Together, all these benefits show how AI can make a big, notable change also in the pharmaceutical field.

AI – Shaping the Future of Pharmaceutical Industry

AI is transforming the pharmaceutical landscape, particularly in areas vital to industrial pharmacists, such as manufacturing, quality control, and distribution. These professionals play a pivotal role in skillfully integrating AI, serving as the human-in-the-loop to enhance efficiency and ensure safety in pharmaceutical operations.

AI elevates the manufacturing process, forecasts maintenance needs, and sharpens quality control. Industrial pharmacists are pivotal in deploying these AI-driven techniques, ensuring that operations are not only effective but also meet high-quality standards and regulatory requirements.

The Role of Industrial Pharmacists

Industrial pharmacists are essential contributors to this technological revolution, actively collaborating with data engineers and scientists. They play a pivotal role in ensuring regulatory compliance, upholding product quality, and leveraging AI to enhance drug development processes, inventory management, and distribution. Industrial pharmacists:

  • are essential in incorporating AI into manufacturing workflows.
  • ensure AI tools align with regulatory requirements and uphold product quality.
  • utilize AI to accelerate and economize the drug development process.
  • leverage AI for more effective inventory and distribution management.
  • analyze data generated by AI systems for informed decision-making in production and quality control.
  • ensuring the quality of pharmaceutical products, they play a crucial role in safeguarding patient safety.
  • leverage AI to identify eco-friendly manufacturing practices, contributing to sustainable pharmaceutical production.

Risks and Challenges

Using AI in the pharmaceutical environment involves navigating risks such as ensuring data privacy and security, maintaining regulatory compliance, addressing biases and ethical concerns, and dealing with the quality and reliability of data. Additionally, there are challenges related to intellectual property issues, integration with existing systems, scalability and maintenance, and dependence on external vendors. To effectively leverage AI benefits while minimizing these risks, a comprehensive strategy encompassing robust data governance, ethical AI practices, ongoing regulatory engagement, and careful technological and organizational change management is essential.

Conclusion

The pharmaceutical industry stands on the brink of a transformative era, driven by the profound potential of AI to reshape its landscape. The key to unlocking this potential lies in the proactive involvement of industrial pharmacists, who are urged to assume a more strategic and leading role in steering innovation.

Traditionally perceived as followers, industrial pharmacists now face a pivotal moment to transition into drivers of change. This isn’t merely a shift in perception; it is a call to action. The integration of AI offers a unique opportunity for pharmacists to shape the future of pharmaceutical care actively and courageously.

In this evolving landscape, industrial pharmacists are not just guardians of compliance but architects of efficiency, adaptability, and innovation. Collaborating seamlessly with AI technologies, they hold the power to propel the industry forward. Despite certain challenges, this collaboration looks promising – it isn`t just compliant and efficient but also dynamic and inventive.

The call to action is clear – pharmacists, especially those in industrial roles, are not merely spectators in this technological revolution; they are the forerunners, charting a course towards a more responsive and innovative pharmaceutical future.

References:

Artificial trends: intelligence in the pharmaceutical industry: analyzing, innovation, investment and hiring

Insights to the Industrial Pharmacist role for the future: A concept paper from EIPG Advisory Group on Competencies, vol 2, 2023

Pizoń J, Gola A. Human–Machine Relationship—Perspective and Future Roadmap for Industry 5.0 Solutions. Machines. 2023; 11(2):203.

Zheng, S. (2023, Nov. 2). “Empowering the pharma workforce.” Pharma Manufacturing.

Contact for further information:
Anni Svala, Vice-President for European Affairs, European Industrial Pharmacists Group, [email protected]


Generative AI in drug development

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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.