Meg Rivers is the former senior editor for 

Meg Rivers

Prior to submitting an investigational new drug (IND) application to FDA, there is much for companies to plan and consider in advance. Collecting the appropriate data and determining just how much data are required to prove a drug product is reasonably safe for initial testing on humans are some of the challenges facing companies in the pre-clinical stage of development and planning for IND activities.

According to Mara Holinger, SVP, regulatory affairs, Veristat, the drug product, biologic, or other proposed assets should be tested in 

to demonstrate that the product is efficacious in a therapeutic model.

“Next, or in parallel, a contract development and manufacturing organization (CDMO) needs to be selected,” Holinger continues. “The drug product formulation needs to be carefully considered and then manufactured per good manufacturing practice (GMP) standards. The drug product will need to undergo toxicology studies, typically in two animal species, for both acute and repeat dose testing. These studies are generally required to be conducted per good laboratory practice (GLP) standards.”

Additional testing that might be required, depending on the product type and indication, includes the following:

It is also essential that all necessary information is collected for the drug substance, the drug product, and the non-clinical and clinical investigations, Paola Tocchetti, senior VP, head of global regulatory affairs, Evotec, explains. Some of the required information includes an investigator brochure, a clinical protocol, and IND sections in electronic common technical document (eCTD) format.

“It is our best practice to have all these documents included in an internal tracking system, an ‘IND tracker’ allowing us to follow the progress of the IND documents preparation according to the set timelines. The IND tracker also enables us to order priorities for any critical paths,” says Tocchetti.

As might be expected, some believe the best time to plan for IND activities is as early as can be managed.

“The regulatory strategy should be in place as early as possible in the development process—ideally at the step when the candidate is nominated for development,” says Tocchetti. “This is then followed by the IND edition, which requires the drug substance, drug product, and non-clinical reports. The health authorities’ regulations are the driving factor for the drug development process; for example, compendial tests from [the United States] and 

and regulated toxicology programs to support the first-in-human study must be performed.”

Meanwhile, Kevin Hennegan, senior regulatory strategist, Veristat, shares that there isn’t a single answer for when to start planning for an IND application, explaining that most companies should think about a regulatory plan once there are data to support a possible clinical benefit for an investigational product.

“To set the IND up for success, I think the two most important things a company can do are to engage an experienced regulatory professional (either an employee or a consultant) who can help you navigate the process; and take advantage of the free advice offered by FDA through the pre-IND meeting program,” Hennegan explains.

But the data are not the only important factor for INDs. A clear presentation of data is also critical. Hennegan adds that the success of an IND submission depends largely on how clearly the data are presented—in addition to complying with regulatory guidance.

“For a regulatory reviewer, it is important that the document that he/she receives is coherently worded, linguistically correct, [and] consistent in terms of content and connection between the different sections. The goal is to provide a document that the reviewer ‘enjoys’ reading,” says Tocchetti. “We also find that the timely and accurate input of all the R&D functions and the integrated management of their different contributions is crucial to ensure the success of an IND submission.”

From a manufacturing perspective, selecting a CDMO is a common pitfall. According to Hennegan, CDMOs can create significant delays in programs if they are low-performing.

“One other pitfall that is sometimes missed is not thinking product quality through all the way to delivery to the patient,” says Hennegan

 “If there is a dilution step or a novel (i.e., investigational) device required for delivery, you will need to provide data that those processes do not adversely affect the product.”

Hennegan further breaks down potential pitfalls into non-clinical and clinical:

: One pitfall is species selection. Costly additional studies can result from failing to perform toxicology studies in a relevant species. Meanwhile, costs can also be increased—without adding value—when selecting burdensome species. Selecting the best species to use should be discussed in a pre-IND meeting.

: Measures must be put in place to mitigate risks to the study subjects—such as frequent vital sign assessments, safety labs, follow-up visits, and rules for stopping a study—which can be done through risk analyses of the first-in-human protocol.

Tocchetti adds the most common pitfalls they have come across:

The reviewer can’t determine if the administration of a drug product in the clinical study is safe due to a lack of information in the IND.

The most recent FDA and European Medicines Agency guidelines aren’t adhered to when designing the nonclinical development.

GMP and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use guidelines aren’t followed, and a reason why the rules were not followed isn’t documented properly.

The quality level of the drug substance and/or drug product quality level isn’t sufficient to administer it to humans.

The findings of toxicology don’t support dosing in humans.

Moreover, one significant misconception about IND applications, according to Holinger, is the turnaround time.

“[I’ve] worked with several sponsors that believe the IND writing and submission process can be completed in a month or two. If a sponsor is starting from scratch (does not have a previous IND to reference), it typically takes four to six months to write an entire IND, pull together all of the documents, and publish/submit the IND,” says Holinger. “Another misconception I’ve come across is the assumption that an IND is a single document. INDs need to comply with eCTD standards including administrative information (module 1), summaries (module 2), CMC/quality (module 3), non-clinical testing (module 4), and clinical (module 5).”

“Companies who excel [in the IND application process] have an integrated and multidisciplinary approach to IND planning and execution,” says Tocchetti. “Thus, a tightly integrated approach and expertly coordinated delivery strategy enable them to carefully perform risk-management and rapid decision-making. The increase in quality allows for timely delivery of a robust and comprehensive regulatory data package for IND submission without ever needing to compromise on quality because of lack of time.”

In addition, the probability of a successful IND is increased by running a pre-IND meeting with the relevant regulatory agency, such as FDA, according to Tocchetti.

Proper planning and project management, according to Erin Flynn, regulatory strategist, Veristat, are also key to excelling in the IND application process.

“Proper planning and project management are critical aspects of IND preparation that are often underrated,” says Flynn. “The IND is a granular collection of documents that can sometimes be composed of upwards of one hundred individual documents, with multiple authors, and often with overlapping review cycles. It is imperative that all authors are aligned on product-specific details in order to maintain consistency throughout the IND and present the best face forward to the FDA. Project management is also key. It can be easy to get off track or overwhelmed by the sheer number of documents to prepare; so, effective tracking and timeline management streamline the IND preparation process.”

When looking ahead to 2022 and beyond, both Holinger, Hennegan, and Flynn predict the number of IND applications for COVID-19 vaccines will decrease. In addition, they predict a potential increase in the number of INDs for the treatment of COVID-19. Furthermore, they anticipate that trends in existence prior to the pandemic will re-emerge, such as an increase in INDs for personalized therapies, along with a general increase in INDs for mRNA-based products.

Meanwhile, Ralf Geiben Lynn, PhD, MBA, senior business development director, Evotec, foresees regulatory changes for large molecules.

“As more and more innovative treatments arise, we expect that the FDA guidance documents in non-traditional non-small molecule fields like biologics or gene-therapy products [to] evolve and mature,” says Lynn. “Thus, it is expected in the future, that the sponsors and specialized CROs will continue to keep up-to-date with these new treatments.”

Where the future of IND applications is headed, only time will tell. But one trend is clear: planning ahead whenever possible has the potential to provide a positive impact on INDs and their success rate.

Pharmaceutical Technology, Pharmaceutical Technology Europe


Volume 46, Number 6
June 2022
Pages: 19–21

When referring to this article, please cite it as M. Rivers, "Early Development Challenges in IND Applications," 

Articles

Collecting relevant data, planning ahead, and communicating with regulatory bodies in pre-IND meeting programs can help companies to avoid roadblocks in IND applications. 

Early Development Challenges in IND Applications

A treatment that has been top-of-mind in recent years is cell and gene therapies (CGTs). Many companies and organizations investigate the potential of this type of treatment to address unmet medical needs. While significant headway has been made, CGTs are in their infancy. But with the potential to cure diseases, CGTs could change the future of treatments for certain conditions.

“[CGTs] offer for the first time the possibility to offer truly disease-modifying treatments, providing options for conditions considered intractable or where the current standard of care is insufficient,” says Bayer AG company sources.

Converting Injectable Therapies to Combination Drug-Device Products

Read this article in the Trends in Manufacturing ebook.

1. S. Fried et al., “Patients With Out of Specification Tisagenlecleucel Can Be Salvaged With Point-of-Care CART-T Cells: An Observational Intention-to-Treat Single-Center Analysis,” presentation at 2021 European Hematology Association Virtual Conference, EHA (Virtual, Aug. 15, 2021).

2. Stasiak, Andrzej. “Gene therapy,” EMBO Reports, March 5, 2001.

3. FDA, “FDA Approves Novel Gene Therapy to Treat Patients with a Rare Form of Inherited Vision Loss,” FDA.gov, Press Release, Dec. 18, 2017.

When referring to this article, please cite it as M. Rivers, “Opportunities for Growth and Breakthroughs for Cell and Gene Therapies,” 

Pharmaceutical Technology Trends in Manufacturing

CGTs offer hope for the future of treatments, but the costly manufacturing, slow turnaround time, and need for supplies hinder progress.

Opportunities for Growth and Breakthroughs for Cell and Gene Therapies

In this episode of the Drug Solutions Podcast, Meg Rivers, senior editor, interviews Joy Aho, senior product manager at Be The Match BioTherapies, about sourcing autologous and allogeneic cell therapies, including discussions on:

Current sourcing options for biopharma companies in the cell therapy space

Key considerations when deciding what sourcing method is best for an individual therapy

Considerations for donor identification and cell collection

Potential collection network strategies for cell therapy, and how these strategies impact quality, agility, and scalability

Vendors for cell therapy and supply chain impacts

Joy Aho, PhD, is the senior product manager at Be The Match BioTherapies, where she develops innovative products and services for the cell and gene therapy industry.

 presents the Drug Solutions podcast, where the editors will chat with industry experts from across the pharmaceutical and biopharmaceutical supply chain. Join us as experts share insights into your biggest questions—from the technologies, to strategies, to regulations related to the development and manufacture of drug products.

Podcasts

In this episode of the Drug Solutions Podcast, Meg Rivers, senior editor, interviews Joy Aho, senior product manager at Be The Match BioTherapies, about sourcing autologous and allogeneic cell therapies.

Drug Solutions Podcast: Sourcing Autologous and Allogeneic Cell Therapies

In early development of medicinal chemistry, there are a lot of considerations, such as determining promising agents and dosage form. 

interviewed Chase Smith, PhD, senior application scientist at Optibrium (a software company for drug discovery), and Kevin Short, director of medicinal chemistry at Verseon International (a clinical-stage pharmaceutical company), who discuss key considerations for medicinal agents in early development, challenges and opportunities in medicinal chemistry, what data to consider when selecting a high-potential drug candidate, and how artificial intelligence (AI) can be harnessed in this process.

: What are key considerations when working with medicinal agents in the early development phase?

 The most obvious general consideration is whether or not there are multiple paths forward. Since the medicinal chemist will inevitably synthesize multiple rounds of compounds in order to optimize physicochemical properties, pharmacologists will need to ensure there are easily accessible and relevant pharmacokinetics and disease models, which will interrogate the compound candidates. There needs to be a functional link between potency in 

efficacy in humans. During all the above, the medicinal chemist constantly needs to pay attention to physicochemical properties, and [consider] whether further optimization retains the original plan to deliver the drug by the desired method (i.e., oral, intravenous, intraperitoneal, etc., route of administration).

 Ideally, one would be able to test any potential drug candidate in a well-proven disease model, allowing you always to identify the best possible compound. Realistically, chemical space is too vast, and good early-stage disease models are often not practical to run due to costs or being too low in their throughput.

Consequently, a phased approach to the different discovery stages is taken where the key criteria shift as the project evolves. Early on, the focus is often only on activity and perhaps selectivity. As things progress, the scope is expanded to include a broader range of ADMET [absorption, distribution, metabolism, excretion, and toxicity] properties before ultimately focusing on PK/PD [pharmacokinetics/pharmacodynamics], safety, and manufacturability at scale.

Early discovery has made great strides in strategies and methods for increasing throughput and maximizing the sampling of chemical space. However, the greatest challenge is moving some of the later-stage indicators (downstream ADMET, PK/PD, safety) earlier in the process. Computational tools are now increasingly contributing to this, with the ever-improving performance of predictive modeling synergizing with idea generation and guided library enumeration capabilities. These tools are facilitating improved virtual screenings, thereby generating higher-quality predictions. Overall, this helps to more rapidly identify high-quality chemical matter while simultaneously avoiding false leads before investing significant resources.

 What do you see as the greatest challenge in medicinal chemistry in early drug development? The greatest area for opportunity?

 One of the primary difficulties is that findings from early-stage assays and measurements often fail to translate to later-stage and vastly more complex systems, such as animal models or even humans. Another problem is that early drug discovery data is inherently noisy due to the uncertainty of the measurements and experimental error. This can lead projects in the wrong direction, wasting time and resources that could be better allocated or lead to missing opportunities.

While there is a lot of hype and promise around AI in drug discovery, much remains unsubstantiated. Still, several proven technologies are pushing the boundaries of what is possible. [Particular] methods are performing well that effectively account for the nature of data in early drug discovery, the relatively high variability and uncertainty in measurements, and the fact that most compounds have only been measured in subsets of assays due to time or cost constraints (sparse data). Furthermore, to build trust and acceptance by scientists, AI technologies need to align with how discovery scientists think and work to achieve greater adoption and significantly increase the rate of success and speed of drug discovery.

 The most obvious challenges relate to diseases with additional obstacles to drug delivery, such as crossing the blood-brain barrier. Finding readily deliverable drugs for Parkinson’s, Alzheimer’s, multiple sclerosis, and other central nervous system (CNS)-related diseases is much more complex than typical non-CNS drug development. At the same time, the challenges in CNS drug development mean treating these conditions is also an arena of great opportunity. As for other types of systemic opportunities in medicinal chemistry, the use of all available data and the application of AI for lead optimization or repurposing existing drugs come to mind.

: What data needs to be considered in selecting a high-potential drug candidate? In what ways can that data best be organized and utilized?

 A great deal of clinical data on marketed drugs and drug candidates is publicly available. There are also some other published data on compounds from both successful and failed medicinal chemistry campaigns. Unfortunately, data from failed campaigns are relatively sparse because corporations have many disincentives to publish that information. Whether or not the volume of data from failed programs exceeds that for successful ones, most agree that all data—including data for failed programs—are valuable to advance the field. The complete data need to be aggregated and anonymized in a usefully available manner to provide incentives for publication and allay concerns that releasing the data will compromise intellectual property.

 The types of data currently collected (such as activity, solubility, metabolic stability, etc.) will most likely not significantly change within the near future. Most early drug discovery programs start from a basic set of early assays and compound measurements designed to provide proof of principle that the idea under consideration has merit. A complicating factor is that the targeted therapeutic area will also influence the perceived importance of one type of data over another. So, it is not the type of data being generated, but instead, it is the quality and quantity of the early data that, if improved, should benefit future programs. Computational tools support drug discovery scientists to make effective decisions based on complex data. This helps to target high-quality compounds for synthesis and testing and quickly focus on the best compound for progression to later-stage studies. In particular, computational approaches that enable translational insights from early data to better predict more complex, later-stage data, such as PK/PD, 

efficacy, and safety outcomes, increase the success rate and speed of discovery.

A requirement is that data are stored in an easily accessible format. Data that are not reliably and easily accessible to computational processes represent a barrier to progress. Essentially, the data become siloed or walled off from deeper analysis, thus limiting their value and impact on the drug discovery process. Inaccessible data also impede potential collaborative efforts between programs, whether internal or external to their organization.

Artificial intelligence in medicinal chemistry

: How can AI be utilized in the medicinal chemistry space?

 There are three key applications of AI in the medicinal chemistry space: 1) prediction of compound properties, based on learning from existing compounds and data; 2) generative chemistry, which generates new compound ideas (structures) to consider in the context of a drug discovery project; and 3) reaction prediction, or retrosynthetic analysis, which can efficiently evaluate synthetic feasibility. Outside of drug discovery, a key development in AI has been deep learning methods that benefit from large, precise, and complete data sets (Big Data) to generate predictive models of unprecedented quality. In contrast, the available data in early drug discovery projects are orders of magnitude smaller than in other fields. Furthermore, drug discovery data are highly variable and sparse due to the fact that most compounds have only been measured in subsets of the full spectrum of assays. However, recent developments in AI methods enable them to generate insights even from these challenging data to highlight opportunities that may have been missed due to uncertain, missing, or inaccurate data. However, the expert medicinal chemist, with experience and knowledge of the chemistry and biology underlying a project, is ideally placed to make educated decisions that can also guide the AI engine. This is the concept of augmented intelligence, whereby human experts and AI algorithms combine to achieve the best outcomes.

 As we collect much larger and more complex data sets from increasingly advanced functionally predictive assays, machine-learning tools could potentially make medicinal chemistry operations more efficient. There are other areas where AI could be useful. For example, functionally interrelated proteins (e.g., kinases, proteases, and G-protein-coupled receptors) and their structurally interrelated inhibitors lend themselves to analysis by AI tools. AI that examines complex structure-activity relationships can also aid in lead optimization. Other AI tools can help integrate the medicinal chemistry development cycle with process chemistry and manufacturing at scale. Although there is a great deal of exuberance around AI today, one significant unanswered question is whether AI will enable medicinal chemists to solve problems that would have been intractable otherwise. That remains to be seen.


Vol. 46, No. 4
April 2022
Pages: 21–22

When referring to this article, please cite it as M. Rivers, “Early Development Medicinal Chemistry: Utilizing Data and Artificial Intelligence,” 

Experts discuss what data to consider when selecting a high-potential drug candidate and how AI can be harnessed in medicinal chemistry drug discovery. 

Early Development Medicinal Chemistry: Utilizing Data and Artificial Intelligence

The bio/pharmaceutical supply chain has been put to the test in recent years. While no one could anticipate the challenges and changes the world would face, industry experts must now seriously consider how their role in the supply chain can be supported to strengthen and stabilize an imperfect supply chain. But what could that look like?

“In an ideal world, I’d like to see life sciences supply chains break down the linear and functional siloed approach that exists today to become a dynamically connected ecosystem that integrates the full supply chain network in a collaborative and optimized way,” says Stephanie David, vice president, Pariveda. “A dynamically connected and integrated ecosystem can enable life sciences supply chains to finally be patient-centric and purpose-driven.”

David describes a patient-centric supply chain as relying on an interconnected digital network that puts the patient at the center of the supply chain and enables real-time, end-to-end visibility of the movement of drug products and other goods within the supply chain network. This way, organizations can quickly sense, react, and predict changes to the demand and supply.

Similarly, Glenn Koepke, GM, network collaboration, FourKites, believes greater end-to-end visibility and smarter paths to patient communities need to be harnessed to reduce waste and accelerate patient outcomes.

“[An ideal supply chain] would master the control tower concept, stitching together complex and siloed supply chains, so organizations have greater visibility and better insights to help them run more efficient operations,” says Koepke. “Real-time visibility platforms are the connective tissue that enables excellence.”

Heather Zenk, RPh, PharmD, president, distribution services and supply chain operations, AmerisourceBergen, adds that a supply chain would ideally have a standardized set of methodologies and infrastructure to share data across the private and public sector supply chains.

“I say this because when AmerisourceBergen and other private sector partners were operationalizing programs with the federal government at the beginning of the pandemic, one of the biggest issues we encountered was that there wasn’t a common language shared between us. Allocation and shortages, for example, take on different meanings depending on where you sit within the supply chain,” says Zenk. “In addition, the public and private sectors were building data sharing capabilities at the same time we were all trying to collectively manage the crisis. So, we took on the effort together and spent the time to build software systems and portals—to create this infrastructure that didn’t exist pre-pandemic—to share information across these two sectors. If we can standardize methodologies, data processes, language, and information sharing for both the private and public sectors, that will help tremendously when the next crisis hits.”

Being proactive, rather than reactive, is a considerable challenge the industry faces to address current supply chain vulnerabilities. Without proactively addressing vulnerabilities, organizations could go into “survival mode,” as David puts it.

“[M]ost, if not all, supply chains are set up primarily for cost efficiency and not resiliency or agility,” says David. “Sourcing of raw materials and lack of visibility into supplier risks, capacity limitations, and cybersecurity are some of the most critical vulnerabilities organizations are being challenged with. Without the ability to effectively illuminate the extended supply network end to end, many life sciences organizations have no other choice than to shift into survival mode and/or incur costly measures to prevent a breakdown in the supply chain that can leave patients without access to their therapies.”

David adds that while organizations have invested in supply chain management systems, many are still relying on spreadsheets and manual processes.

In addition to inherent flaws in the foundational setup of the supply chain and the need for fewer manual processes, there are functional shortcomings as well—particularly with regards to cold chain storage and distribution.

“The biggest vulnerability in today’s bio/pharma supply chain is product protection and loss as a result of temperature fluctuations or security breaches. Temperature has been a particular focus during the pandemic,” says Koepke. “But cold chain failure in the supply chain can be catastrophic for COVID vaccines and beyond. Pharmaceutical executives have identified up to 20% spoilage rates for non-COVID-19 vaccine wastage due to temperature failures in transport. The World Health Organization (WHO) reports that more than half of vaccines are wasted worldwide, and the United Nations claims that smarter cold chain management strategies could have saved a billion vaccines during the pandemic” (1).

Other supply chain vulnerabilities, according to Koepke, include product protection; supply and demand fluctuations; operating costs and penalties; and freight traceability.

Although there are vulnerabilities that need to be addressed in the bio/pharma supply chain, there are other areas where the industry is tackling challenges head-on.

“Pharma and biopharma companies are very robust with enterprise resource planning (ERP) technology and are often highly integrated globally. This is driven by government regulations and requirements,” says Koepke. “In addition, these companies invest heavily in network planning to create as much consistency as possible. While this allows for as much mitigation as possible, even companies that have created a buffer will face disruptions.”

Additional headways where the industry has taken steps to improve the supply chain, according to David, include achieving end-to-end, real-time visibility; having enriched external data; digitizing parts of the supply chain; and streamlining supply chain processes by using artificial intelligence (AI), sensors, and analytic solutions.

“But more importantly, I believe that the supply chain’s robustness will depend on the appetite for innovation and the ability of life sciences supply chain leaders and the skills of their organization (people) to drive the acceleration and adoption of digital capabilities required to effect change across the end-to-end supply chain,” says David. “Because the supply chain is the operational backbone of life sciences companies, it is important that supply chain leaders can connect and clearly articulate the value and business outcomes of these initiatives to the wider enterprise—sales, marketing, operations, finance, etc.”

The pandemic shook up the global supply chain, highlighting problem areas that needed to be addressed. But once these pain points were illuminated, the industry got to work.

“Before the pandemic started, the life sciences supply chain was already a fragile ecosystem due to the heavy reliance on outdated and manual-human intensive manufacturing and supply chain processes and systems,” says David.

According to David, existing systemic challenges and vulnerabilities that the pandemic exposed include fragmentation along the value chain, lack of shared data and information, lack of supplier visibility, and insufficient implementation of next-generation planning systems and capabilities. David adds that the old supply chain model relies on “foreign operations with tax advantages, dependent intellectual property, and inventory buffers as a strategic response to manage supply chain risk and long lead times.”

Turnaround time is one key area impacted by the COVID-19 pandemic for the better.

“The pandemic has catapulted pharmaceutical supply chains forward to move faster. The pharmaceutical supply chain is making a sharp pivot from a just-in-time model to a test in agility, resilience, and disruption that goes far beyond vaccinations to navigate fast-growing industry complexities,” says Koepke.

AmerisourceBergen, specifically, pivoted to launch therapies on a shorter timeline, having gone from a product launch with an 18- to 24-month timeline for manufacturing to 30–45 days. Zenk adds that therapies with COVID-19 indications may have a quicker turnaround time.

Other changes within AmerisourceBergen focused on improved communication. The company took steps to build relationships with federal agencies, nonprofits, and other private sector supply chain partners since the start of the pandemic, according to Zenk.

In addition, AmerisourceBergen changed its approach to sourcing and risk management, particularly for products that are finished abroad or have key starting materials, which have had limited availability throughout the pandemic.

“For example, we’re working with a diverse set of partners and built a database to ensure our sourcing of products isn’t overly indexed from one particular country or one part of the globe,” says Zenk. “We also rethought our allocation methodology. Pre-pandemic, distributors used a ‘fair share’ allocation model. We quickly shifted to more dynamic allocation procedures, which allowed us to respond more directly to the evolving situation and stabilize inventory across heavily impacted points of care.”

The prioritization of the supply chain has also been positively impacted by the pandemic.

“A few ways that life science supply chains have improved or rather changed since the start of the COVID-19 pandemic include that supply chain is now a key priority for the C-suite and that companies have adapted the supply chain footprint and adjusted effectively to new ways of working, boosted inventories, and ramped their digital and risk-management capabilities,” says David.

Zenk adds: “There have been countless lessons learned, and it’s critical for supply chain stakeholders to carry these lessons from the pandemic forward so we can shorten the learning curve for future issues.”

1. D. Cameron, “Covid-19 Vaccine Delivery Will Present Tough Challenge to Cargo Airlines,” 


Vol. 34, No. 3
March 2022
Pages: 23–24

When referring to this article, please cite it as M. Rivers, "Supply Chain Vulnerabilities in a Post-Pandemic World," 

Although the bio/pharma supply chain has vulnerabilities that still need to be addressed, the COVID-19 pandemic has left lasting effects—some of them for the better. 

Supply Chain Vulnerabilities in a Post-Pandemic World

In this episode of the Drug Solutions Podcast, Chris Spivey, editorial director, and Meg Rivers, senior editor, interview Sean Tucker, PhD, chief scientific officer of Vaxart, on developing oral recombinant vaccines, including developing this type of vaccine and how it differs from injectable vaccines, an inside look into the manufacture for clinical trials, what pathogens oral recombinant vaccines may be a better treatment option for, longevity of protection, and how this type of vaccine could impact the pharmaceutical industry.

In this episode of the Drug Solutions Podcast, Chris Spivey, editorial director, and Meg Rivers, senior editor, interview Sean Tucker, PhD, chief scientific officer of Vaxart, on developing oral recombinant vaccines.