Feliza Mirasol is the science editor for 

Feliza Mirasol

A slew of multifaceted challenges are facing the biopharmaceutical industry's advanced cell therapy sector, including product quality, regulatory compliance, and manufacturing scalability. Traditional autologous cell therapy workflows, often reliant on manual processing, inherently introduce such risks as contamination, human error, and data integrity vulnerabilities, all of which directly impact patient safety and therapeutic efficacy (1).

A persistent misconception within the biopharma industry suggests that advancing quality and compliance invariably necessitates increased costs or reduced output. However, as Ossama Eissa, chief operating officer at Cellares, said during his presentation, “Upgraded Facilities to Assure Quality and Compliance,” at the 

Parenteral Drug Association (PDA) Regulatory Conference 2025

, Eissa quoted, "Any intervention or stoppage during aseptic processing can increase the risk of contamination" (2).

He further elaborated on this principle, stating, "the more intervention you have the more risk you might incorporate, and the more controls you need to [exert] to make sure that you reduce those risks." This perspective advocates for a paradigm shift involving strategic investment in integrated automation, which can simultaneously elevate quality and compliance standards while enhancing productivity and cost-efficiency for biopharma manufacturers.

How does automating cell therapy manufacturing provide aseptic assurance and scalability?

The core of biopharma manufacturing, particularly for autologous cell therapies, involves complex, multi-step processes, such as enrichment, selection, activation, transfection, expansion, and formulation, Eissa enumerated in his talk. These traditionally manual operations necessitate frequent injections, sterile welds, material transfers, and exhaustive documentation, each presenting a potential point of failure for contamination or data integrity.

Taking an automated approach can address these challenges, as with Cellares’ Cell Shuttle platform technology. Eissa pointed out that this platform employs a single-use consumable cartridge that integrates all essential unit operations, allowing patient material to remain within a closed system from initial loading until harvest, significantly reducing manual intervention and associated risks. The cartridge’s passive components are activated by a bioprocessing system, which provides electric motors, load cells, and peristaltic pumps. Furthermore, key modules within the cartridge include a centrifugal elutriation system for cell enrichment, magnetic selection and electroporation flow cells, a perfusion-enabled bioreactor system, and formulation containers.

A fluidic bus system facilitates software-defined transfer of cells and reagents between modules, offering workflow flexibility within a single cartridge design. By processing up to 16 cartridges in parallel within a compact footprint, this closed, automated system significantly improves sterility assurance and quality by minimizing manual movements and aseptic risks, while scaling manufacturing capacity from tens to hundreds of patients annually, Eissa explained in his talk. The benefit to biomanufacturers, particularly in cell therapy, is that this closed system directly translates to reduced contamination rates, higher product consistency, and the ability to meet increasing patient demand, Eissa said.

How do integrated QC solutions benefit data integrity and throughput?

Quality control (QC) in cell therapy manufacturing typically constitutes a substantial operational team, often exceeding manufacturing personnel in autologous settings due to the individualized nature of patient batches. Conventional QC processes involve extensive manual handling for scheduling, reagent and sample preparation, assay execution, and data verification, all of which are susceptible to variability and human error, Eissa emphasized.

Automated QC platforms (e.g., Cell Q, Cellares) integrate commercial off-the-shelf instruments—including cell counters, flow cytometers, centrifuges, plate readers, incubators, and polymerase chain reaction systems—with a robotic liquid plate handler. As Eissa explained, this automation streamlines the majority of in-process and release testing assays, from sample loading to automated data upload into laboratory information management systems. The benefits to biomanufacturers are substantial and include automated generation of electronic batch records for thousands of doses annually, accelerated analytical method transfer, improved assay robustness, reduced manual labor, and significantly higher data quality and consistency. This integration enhances regulatory compliance and provides a reliable audit trail, which is critical for product release and patient safety, he added.

What role does peripheral system automation play in the push for facility-wide operational excellence?

Beyond core manufacturing and QC, automation in peripheral facility systems further contributes to overall quality, compliance, and cost efficiency. Automated warehousing systems, similar to those found in other high-volume industries, utilize smart storage logic and barcode scanning for automated material loading and retrieval. This approach reduces picking errors and minimizes inventory discrepancies and wrong shipments, while automatically updating inventory systems via enterprise resource planning integration, Eissa highlighted.

By storing barcoded materials in standardized boxes and automating retrieval through a human-machine interface, these systems enhance operator safety, reduce the likelihood of selecting incorrect materials, and improve traceability and security of valuable patient products. Collectively, these peripheral system upgrades reduce variability, mitigate manipulation risks, improve data integrity, and minimize aseptic interventions across the entire facility, thereby fundamentally assuring product quality and compliance while simultaneously increasing productivity and reducing operational expenditures, Eissa concluded.

The PDA Regulatory Conference 2025 was held on Sept. 8–10 in Washington, D.C.

1. Eissa, O. Upgraded Facilities to Assure Quality and Compliance. Presentation at PDA Regulatory Conference 2025, Washington, DC., Sept 8–10, 2025. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://80a7ba3d04f8b71aa576-301909dc4570c350a1649a6d39e3ef3b.ssl.cf1.rackcdn.com//3180113-2786354-002.pdf
2. FDA. 

Guidance for Industry, Sterile Drug Products Produced by Aseptic Processing—Current Good Manufacturing Practice

Articles

The biopharmaceutical industry's advanced cell therapy sector faces challenges in product quality, regulatory compliance, and manufacturing scalability. Traditional manual processes in autologous cell therapy introduce risks like contamination and data integrity issues. Automation, such as Cellares' Cell Shuttle platform, can enhance aseptic assurance and scalability by integrating unit operations into a closed system, reducing manual intervention. Automated quality control solutions improve data integrity and throughput, while peripheral system automation enhances facility-wide operational excellence, ensuring product quality and compliance while increasing productivity and reducing costs.

Advanced automation in biopharma facilities secures product quality, assures compliance, and improves efficiency.

PDA 2025: Optimizing Biomanufacturing and Automation for Quality and Scalability

With the bio/pharmaceutical industry undergoing a significant digital shift, artificial intelligence (AI) is playing an increasingly vital role in achieving operational excellence and bolstering quality maturity. A strategic and well-governed approach to AI adoption is not merely an advantage but a necessity for enhancing efficiency while maintaining rigorous good practices (GxP) compliance, said Vinny Browning, executive director of Quality Assurance, Amgen, who spoke at the 

, occurring Sept. 8–10 in Washington, DC.

In his talk, “Digitization (Artificial Intelligence) for In-House Operational Excellence,” Browning outlined a framework for integrating AI into bio/pharmaceutical manufacturing operations, focusing on strategic foundations, quality management system (QMS) integration, robust risk management, and practical applications (1).

What strategic foundations are necessary for AI adoption in regulated manufacturing?

A fundamental aspect of AI integration is a commitment to continuous learning and adaptation, treating evolving AI guidance like any critical company update, Browning stated in his talk. Companies must critically assess each AI application, distinguishing whether it serves as a job aid—assisting human tasks—or operates autonomously. This distinction profoundly influences the approach to system qualification and validation.

A manufacturer’s strategy for qualifying and validating AI systems must be meticulously documented within standard operating procedures (SOPs) to establish a clear, standard process, Browning emphasized. Crucially, comprehensive training is essential, not just for end-users to grasp basic functionalities, but also for staff who may need to articulate the company’s specific AI processes to auditors or inspectors during regulatory reviews. Browning noted that practicing these explanations will help ensure clarity and confidence in the industry’s digital endeavors.

Effective integration also demands consideration of AI's architectural fit and robust governance within a company’s QMS. Data integrity is paramount, and inconsistencies in data naming or definitions across systems, such as “time” versus “TM”, can undermine AI effectiveness, explained Browning. A clear gating process is necessary, acknowledging that an AI activity initially deemed non-good manufacturing practice (GMP) adherent might later become GMP-relevant, thus requiring specific controls based on identified risks. Continuous monitoring and assessment of AI tools are also essential, he added, with adjustments made if risks or guidelines change, mirroring the approach to computer system software assurance. The broader bio/pharma landscape already shows AI's promise, with initiatives exploring its use in gene writing, CAR-T preclinical development, vaccine research, and circular RNA therapeutics, highlighting its transformative potential across the industry (2,3).

What risk management and practical applications can be applied to ensure manufacturing quality?

Any new AI idea or system intake must undergo a structured risk management framework. This includes defining the idea, conducting a thorough system-level risk assessment that covers security (access control, third-party involvement) and architectural setup (on-site, cloud, remote), and selecting appropriate controls to mediate identified risks. These decisions should be documented in a risk registry. Continuous monitoring of the deployed tool is critical; any change in scope or emergence of new risks necessitates re-evaluation and re-mediation. The ultimate goal is to ensure AI systems are safe, secure, resilient, expandable, integratable, privacy-enhanced, fair (with managed bias), accountable, and transparent, Browning said.

In addition, AI offers significant potential to pull insights and facilitate faster decision making by aggregating data from multiple systems, which drastically reduces the manual effort traditionally required. While specific examples include regulatory surveillance and supplier monitoring, the principle of automating analysis and identifying gaps can be directly applied to manufacturing operations. For instance, Browning explained, AI could streamline annual product reviews by compiling data on deviations, complaints, and change controls, quickly identifying critical issues. Similarly, for periodic reporting, AI can pull information from various systems to create first drafts of reports, such as those related to supplier performance.

These applications create considerable efficiencies, saving numerous hours of manual work, Browning pointed out. He noted, however, that it is imperative that all AI-generated information be human-verified. AI can serve as a powerful tool to support individuals, but it is not meant to replace the essential human oversight for verification and compliance. This "human in the loop" rigor is critical to remain compliant and ensure quality, Browning said.

Browning concluded that there are three core pillars on which the strategic integration of AI into bio/pharma manufacturing quality hinges: training, governance, and oversight. He stressed that investing in staff training, establishing robust governance for data integrity and process assessment, and maintaining stringent human quality oversight are non-negotiable.

As the industry embraces digital transformation, Browning notes the prudency of piloting initiatives rather than attempting to implement AI all at once. Taking it slow allows for adaptability and the willingness to adjust strategies if initial trials are not successful. By meticulously following these principles, the industry can better harness AI to drive operational excellence, enhance quality, and ensure sustained compliance in drug manufacturing endeavors.

1. Browning, V. Digitization (Artificial Intelligence) for In-House Operational Excellence. Presentation at PDA Regulatory Conference 2025, Washington, DC., Sept 8–10, 2025. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://80a7ba3d04f8b71aa576-301909dc4570c350a1649a6d39e3ef3b.ssl.cf1.rackcdn.com//3180120-1718426-001.pdf
2. Dixit, S.; Kumar, A.; Srinivasan, K.; et al. Advancing Genome Editing with Artificial Intelligence: Opportunities, Challenges, and Future Directions. 


3. Bäckel, N.; Hort, S.; Kis, T.; et al. Elaborating the Potential of Artificial Intelligence in Automated CAR-T Cell Manufacturing. 

The biopharmaceutical industry is increasingly integrating artificial intelligence (AI) to enhance operational excellence and quality maturity. A strategic approach to AI adoption is essential for maintaining efficiency and compliance with good practices (GxP). Key elements include continuous learning, robust governance, and risk management. AI's potential spans gene writing, CAR-T development, and vaccine research. Effective AI integration requires a structured risk management framework, human oversight, and comprehensive training. Piloting initiatives and adapting strategies are recommended for successful AI implementation in drug manufacturing.

AI can offer a strategic blueprint for GxP compliance, risk mitigation, and human-led operational excellence.

PDA 2025: Strategic AI Adoption in Bio/Pharma Manufacturing

On the cusp of a profound transformation, the bio/pharmaceutical industry is driven by an exponential surge in data generation and the sophisticated capabilities of artificial intelligence. With approximately 400 exabytes of data, equivalent to 18,000 trillion books, produced globally each day—much of it unstructured—the challenge and opportunity for leveraging this information are immense, said 

, co-founder and chief scientific officer, Aizon, at the 

 occurring Sept. 8–10 in Washington, DC.

Data Governance to Ensure Regulatory Compliance, Data Protection, and Future Readiness for Advanced Use Cases

,” Manzano pointed out that this data deluge, combined with the widespread availability of computing power and data storage, is fueling an artificial intelligence (AI) renaissance that promises to redefine drug discovery, development, and manufacturing. AI is proving to be a scientifically validated tool, even playing a crucial role in Nobel Prize-winning research in chemistry and physics in 2024, demonstrating its capacity to solve complex problems and model intricate biological processes (1). Understanding and strategically implementing data-driven AI is no longer optional but an imperative for enhancing safety, quality, and efficiency, ultimately benefiting the patient.

How can bio/pharma operations maximize data value?

At its core, AI is a mathematical tool, Manzano explained in his talk. However, AI’s efficacy hinges entirely on the quality and management of data. Data in the pharmaceutical context extend far beyond traditional numbers and lists, encompassing diverse unstructured images, sounds, and continuous values collected from various systems.

Regulators are keenly aware of this, frequently referencing data and metadata in AI guidelines, emphasizing their critical role in transforming raw information into actionable knowledge. This focus is not without reason, Manzano said, emphasizing that more than 25% of warning letters issued by FDA since 2019 have cited data accuracy issues, a complex problem that continues to challenge the industry. This concern is further underscored by 

® Group highlighting common FDA 483 findings and strategies to prevent regulatory non-compliance, often rooted in data management deficiencies (2). For bio/pharmaceutical companies, ensuring robust data integrity and compliance is paramount, as regulatory actions can significantly impede progress and public trust.

The journey to effective AI implementation is heavily weighted toward data preparation, consuming an estimated 80% of an AI project's time. This rigorous preparation ensures that data are findable, accessible, interoperable, and reusable, known as the FAIR principles, which are essential for quality AI outcomes. As more data is integrated, the potential for knowledge extraction and sophisticated AI models increases, yet this also adds layers of complexity to data and model management, Manzano said in his talk.

What AI-driven innovations are the industry seeing in bio/pharmaceutical manufacturing and development?

The practical applications of AI across the bio/pharmaceutical lifecycle are profound. Consider the management of human plasma, a highly variable raw material for which donations cannot be refused and establishing full product genealogy is exceptionally difficult, Manzano highlighted. AI can be applied here to integrate batch record information with data from other systems, such as programmable logic controllers and online controls, which transforms these diverse data into knowledge that can guarantee expected yields. Through AI, crucial process variables, such as pH in plasma fractionation, can be identified and adjusted to achieve target outcomes. This capability moves beyond trial-and-error, enabling predictive models that recommend optimal process parameters based on raw material characteristics and desired yields (3,4).

Beyond manufacturing, AI is also advancing quality control in novel therapies. For instance, the Parenteral Drug Association, in collaboration with the European Medicines Agency, is utilizing AI for quality control in advanced therapy medicinal products, allowing for the prediction of batch success an hour in advance (5). This collaboration provides critical lead time for decision making without replacing human labor or traditional lab testing, enhancing efficiency and patient safety for those awaiting these crucial treatments, Manzano said.

"Everyone in this room, we are working for pharma, but we are [also] working for patients, so we have to [always] have in mind that everything we do is because there is a patient waiting,” he emphasized in his talk.

The industry's embrace of digitalization is progressing, Manzano concluded, pointing to a recent survey indicating an average maturity score of 3.5 out of 5. That is a notable increase from 2.6 in 2019. While challenges such as cybersecurity compliance and budget constraints remain top concerns for the industry, the perceived benefits are clear, Manzano asserts: respondents anticipate significant improvements in process efficiency and quality through digitalization, especially in areas such as production, research, and quality control. AI, when applied with scientific rigor and supported by high-quality, well-managed data, offers a significant opportunity to navigate the inherent complexities and variability of bio/pharmaceutical operations, ensuring that the ultimate focus remains on the patient.

1. Longhi, S.; Ventura, S.; Macedo-Ribeiro, S.; et al. When Artificial Intelligence Meets Protein Research [Version 1; Peer Review: 2 Approved, 1 Approved with Reservations]. 

New Year, Same Challenges: Pharma’s Ongoing Struggle with FDA 483s

Quality and Regulations 2025 eBook August 2025.
3. IDBS. Enhancing Process Performance with AI in Bioprocessing. Blog Post. May 22, 2025. 

https://www.idbs.com/knowledge-base/enhancing-process-performance-with-ai-in-bioprocessing/


4. Cheng, Y.; Bi,X.; Xu, Y.; et al. Artificial Intelligence Technologies in Bioprocess: Opportunities and Challenges. 


5. Manzano, T. PDA Europe and EMA Unite. 

https://www.pda.org/pda-letter-portal/home/full-article/pda-europe-and-ema-unite

The bio/pharmaceutical industry is undergoing a transformation driven by data generation and AI capabilities. With vast amounts of unstructured data produced daily, AI is becoming essential for drug discovery, development, and manufacturing. Effective AI implementation requires rigorous data preparation, adhering to FAIR principles. AI applications in manufacturing and quality control are enhancing efficiency and patient safety. Despite challenges like cybersecurity and budget constraints, digitalization is advancing, with significant improvements anticipated in process efficiency and quality. The industry's focus remains on benefiting patients through these technological advancements.

Bothe data integrity and quality of data are critical for drug discovery, manufacturing efficiency, regulatory compliance, and patient safety.

PDA 2025: How Data-Driven AI Transforms Quality Control & Development

rare-disease drug development is advancing on the back of FDA incentives, priority review vouchers, and a growing recognition of unmet needs

Combination of Regulatory Incentives and Patient Advocacy Drive Rare-Disease Drug Development 

The field of rare-disease drug development is rapidly evolving on the back of regulatory incentives and a growing recognition of unmet patient needs. Both biotech companies and large bio/pharmaceutical firms are increasingly focusing on rare diseases, thanks to programs such as orphan drug designations, which offer benefits such as market exclusivity, reduced regulatory fees, and tax credits. These incentives help offset the traditionally high costs of developing treatments for small patient populations, says Dan Williams, CEO of SynaptixBio, in an interview with the 

“If you can improve a quality of life, [patients] can tell you how that will really affect them. And that kind of input is extremely valuable for moving the drug process [and] development process forward,” Williams says.

Williams notes that a significant development in the rare disease space is FDA’s priority review voucher system, particularly the 

 designation. This program awards vouchers to companies that develop drugs for rare pediatric conditions, allowing them to expedite the review process for future drugs or sell the voucher to other companies. These vouchers have fetched prices as high as $150 million, providing substantial financial returns that can be reinvested in further research, Williams explains.

Collaboration with patient advocacy groups has also become essential, he stresses, as their firsthand insights help shape drug development to better meet patient needs. This partnership not only accelerates progress but ensures that new therapies have a meaningful impact on patients’ lives.

Dan Williams, PhD, studied biochemistry and physiology at the University of Dundee, after which he worked his way up to senior scientist—managing a cell research group and preclinical development. Following this, he moved to drug development, focusing on the organization and management of both manufacturing and clinical trials. Williams then moved to Adaptimmune, developing cell therapies. He set up the development groups, project managing preclinical research and clinical trials, before becoming vice-president of Research Operations. From there, he moved to Meatable as chief product officer. Williams co-founded SynaptixBio in 2021.

Videos

Dan Williams, CEO of SynaptixBio discusses drivers of rare disease drug development, including FDA incentives, priority review vouchers, and a growing recognition of unmet needs. 

Combination of Regulatory Incentives and Patient Advocacy Drive Rare-Disease Drug Development (Part 2)

The European Medicines Agency (EMA) has granted orphan designation to rilzabrutinib, an investigational oral Bruton’s tyrosine kinase (BTK) inhibitor from Sanofi (1). The designation applies to the treatment of IgG4-related disease (IgG4-RD). Orphan designation is granted to therapies targeting rare, serious, or debilitating conditions affecting no more than five in 10,000 people in the European Union, underscoring the unmet medical need in this patient population (1).

Clinical data support potential in rare immune-mediated disorders

EMA’s decision is based on results from a Phase II clinical trial (NCT04520451) in which patients received rilzabrutinib for 52 weeks (2). The study reported reductions in disease flare frequency, improvements in other clinical markers, and decreased reliance on glucocorticoids. According to the study data, safety outcomes were consistent with those of previous rilzabrutinib studies, with no new safety signals identified. These results were presented at the 2025 European Alliance of Associations for Rheumatology Congress, held on June 11–13 in Barcelona (1).

Rilzabrutinib is being investigated across multiple rare immune-mediated and inflammatory diseases. In addition to IgG4-RD, the drug has orphan designations for treating immune thrombocytopenia in the United States, European Union (EU), and Japan, as well as for warm autoimmune hemolytic anemia, sickle cell disease, and IgG4-RD in the US (1). In addition, fast track designation has been granted in the US by FDA for both immune thrombocytopenia and IgG4-RD.

Regulatory reviews for rilzabrutinib in immune thrombocytopenia are ongoing in the US, EU, and China. A regulatory decision is expected from FDA by Aug. 29, 2025 (1).

Mechanism and Implications for pharmaceutical development

Rilzabrutinib’s mechanism of action targets BTK, a signaling protein expressed in B cells, macrophages, and other innate immune cells (1). BTK is implicated in multiple immune-mediated disease pathways. Rilzabrutinib’s reversible covalent binding approach, which is enabled by Sanofi’s Tailored Covalency technology, is designed to maintain BTK selectivity while potentially minimizing off-target effects.

The development of rilzabrutinib reflects two broader trends in drug manufacturing and discovery: the application of structure-based drug design to improve target specificity, and the strategic expansion of BTK inhibitors beyond oncology into autoimmune and inflammatory indications. Orphan designation status also provides incentives, such as market exclusivity and regulatory support, which can influence development timelines and commercialization strategies.

IgG4-RD: challenges in diagnosis and treatment

IgG4-RD is a progressive, chronic immune-mediated condition that can affect nearly any organ, causing irreversible organ damage and potentially fatal outcomes (3). The disease course often includes recurrent flares marked by exacerbated symptoms. Although U.S. prevalence is estimated at eight cases per 100,000 adults annually, global epidemiology remains unclear due to diagnostic challenges (3).

Given the limited treatment options and the need to balance efficacy with long-term safety in chronic immune-mediated disorders, pharmaceutical advances in this area carry significance for both patient care and manufacturing innovation. The development of rilzabrutinib demonstrates how targeted immune modulation strategies may open new therapeutic pathways for rare and difficult-to-treat conditions.

Sanofi’s Rilzabrutinib Earns Orphan Designation in the EU for IgG4-Related Disease

. Press Release. Aug. 14, 2025.
2. National Library of Medicine. Open Label Two-Arm Study to Evaluate Rilzabrutinib in IgG4-RDParticipants. 

, last updated Feb. 21, 2025 (accessed Aug. 15, 2025). 

https://clinicaltrials.gov/study/NCT04520451


3. Wallace, Z. S.; Miles, G.; Smolkina, E.; et at. Incidence, Prevalence and Mortality of IgG4-Related Disease in the USA: A Claims-Based Analysis of Commercially Insured Adults. 

 (7), 957–962. DOI: 10.1136/ard-2023-223950



Sanofi's investigational oral BTK inhibitor, rilzabrutinib, has received orphan designation from the EMA for treating IgG4-related disease, highlighting its potential in rare immune-mediated disorders. This decision follows promising Phase II trial results showing reduced disease flares and improved clinical markers. Rilzabrutinib is also being explored for other conditions like immune thrombocytopenia and warm autoimmune hemolytic anemia. Its mechanism targets BTK, a key protein in immune pathways, and reflects trends in drug design and BTK inhibitor expansion beyond oncology into autoimmune diseases.

Orphan status was granted based on Phase II data showing that treatment with rilzabrutinib reduced flares and improved markers while demonstrating consistent safety.

EMA Grants Orphan Designation to Rilzabrutinib for IgG4-Related Disease

interest and investment in rare disease drug development, interview with SynaptixBio CEO, Dan Williams

The Evolving Landscape of Rare Disease Pharmaceuticals (Part 1)

Rare disease drug development is emerging as a focal point for innovation and investment amid an evolving landscape of pharmaceutical technologies, according to 

® Group. In the interview, Williams highlights the unique challenges and opportunities in targeting such ultra-rare conditions as TUBB4A-related leukodystrophy, a disorder affecting the myelination of nerves in children.

Williams’ experience spans the full spectrum of drug development, from bench science to clinical trials and corporate operations, providing insight into the complexities of bringing novel therapies to market. “About 80% of rare diseases are genetic and have to do with modifications of genes,” he notes, emphasizing the potential for targeted therapies using advanced technologies. For professionals in drug manufacturing and development, the rare disease sector offers scientific challenges as well as significant incentives, including accelerated regulatory pathways and high market value for successful therapies. This shift is drawing increased attention from investors and companies alike, signaling a broader trend toward precision medicine and the rapid advancement of pharmaceutical technology.

Drug development for rare diseases leverages genetic targeting and drives innovation and investment in pharmaceutical technology as well as precision medicine, according to Dan Williams, PhD, CEO, SynaptixBio.