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Drug Solutions

In this episode of the Drug Solutions podcast, Jill Murphy, Editor, talks to Adam C. Fisher, PhD, director of Science Staff and Immediate Office/Office of Pharmaceutical Quality at the Center for Drug Evaluation and Research/FDA, about point of care, patient manufacturing, and new innovative technologies in the industry.

, is the Director, Science Staff in the Office of Pharmaceutical Quality (OPQ) in the Center for Drug Evaluation and Research at the FDA. Dr. Fisher focuses on engaging FDA stakeholders and supporting advanced pharmaceutical manufacturing technologies. At the FDA, he has served as a primary and secondary reviewer of Abbreviated New Drug Applications (generics) and Drug Master Files, a Team Lead, a subject matter expert on complex drug substances and advanced biomanufacturing, and a liaison to the United States Pharmacopeia BIO1 Expert Committee. He joined the FDA in 2014 as a chemical engineer with expertise in the synthesis of biomolecules. Dr. Fisher’s work prior to joining the FDA focused on the microbial production of proteins and glycoproteins. Dr. Fisher was the co-founder and Chief Science Officer of a startup company focused on microbial technologies for the production of glycoproteins. He earned his B.S. degree at the University of Maryland College Park (Chemical Engineering) and his Ph.D. at Cornell University (Chemical & Biomolecular Engineering).

 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

Adam C. Fisher, PhD, director of Science Staff and Immediate Office/Office of Pharmaceutical Quality at the Center for Drug Evaluation and Research/FDA, discusses point of care and patient manufacturing with Pharmaceutical Technology editor Jill Murphy. 

Drug Solutions Podcast: Point of Care and Patient Manufacturing 

Christopher Burgess, PhD, is managing director, Burgess Analytical Consultancy Limited, Barnard Castle, Co Durham, UK.

Many may wonder at the number of analytical chemists who have faced challenges from management who demand answers from analytical results without explanation of their statistical significance. For the analytical chemist, however, results are never just about getting an answer. For any analytical procedure’s reportable value, there is only a most probable estimate of the true value with an associated interval of confidence. Unfortunately, this fact is not always readily appreciated by management. Perhaps they would prefer to have precise ‘lies’ than the inexact ‘truth.’

So, what might be the root cause of this problem?One suggestion is that some members of management don’t feel comfortable with the idea of uncertainty at all. This attitude probably stems from a fundamental lack of understanding that all measurements and their derived values are subject to error, and that this error is normal (usually). I hope that I may be excused the pun!

Suppose that management can be convinced of the reality of error and uncertainty in analytical measurements. Hence, there will be a necessary requirement to minimize or remove as much error as economically feasible. What can be done about it? As a first step, the potential sources of error within analytical processes must be identified and their individual contributions to that error must be quantified, as illustrated in 

 Error sources and types for analytical procedures.

Gross errors are such that only abandonment of the analysis and a fresh start is an adequate cure.For example, if a portion of the sample is spilt or an instrument has been improperly set up, then no statistical methodology can be applied.Random errors occur on either side of the reported result and affect precision (repeatability, intermediate precision, and reproducibility).This type of error may be thought of as the noise element in the measurement.Systematic errors relate to all the results being biased in one direction, either too high or too low, and hence affect accuracy (closeness to the true value).Random and systematic errors are amenable to statistical evaluation.

Process mapping as an aid to error evaluation

It has long been standard practice to draw process maps for many business processes. However, this is strangely not so common for laboratory analyses. If one can draw a process, one gain an objective understanding of the technical and procedural steps involved. Process mapping tools have long been available since the early days of Six Sigma evaluations, including flow charts and the associated Ishikawa diagrams, for example.

These tools and techniques are less well applied in the laboratory, which is unfortunate. Since the rise of data integrity concerns by regulators and the necessity to assure demonstrable control in analysis, however, their use is increasing.

The principle of starting simple to understand the process is a good one. Learning to walk before attempting running is advisable. This article illustrates the mapping concept with a simple manual volumetric titration involving the standardization of a sodium hydroxide (NaOH) solution with a certified reference material (CRM) of potassium hydrogen phthalate (KHP).

First, a basic process flow can be drawn, as shown in 

. The question then must be asked: is this sufficient for the required purpose? Ideally, process would need to be drawn in more detail to identify the possible error sources and possible error types. A more detailed process flow has been drawn in 

 and expands the basic process flow from 

 Basic process flow for the manual titration. C

is concentration of the sodium hydroxide solution.

 Error source mapping and process flow for a simple manual titration. CRM is certified reference material. NaOH is sodium hydroxide.

With the details now clear, it is reasonable to suppose that the manual operations conducted by the analyst pose the greatest potential for error. The assumption can be made that no gross errors have occured and that the proper precautions have been taken to protect the NaOH solution from possible contamination with carbon dioxide (CO2), and that the reference standard is 100% pure.

Consideration of the manual procedure clearly shows that there could be systematic errors by the analyst in the burette readings as well, because the analyst determines the endpoint visually.In addition, there could be systematic errors in the burette itself, despite the burette being Grade A certified. Classical volumetric analysis requires analyst skill and experience, which is not as readily available as it was in the days before instrumental analysis became widely available.

, one can calculate the concentration of the NaOH solution (CNaOH) as 0.1019 mol L

. However, one must ask, how accurate is this manual result, and what confidence can be ascribed to its value?

The answer to these questions will be explored in the latter part of this article.

Automated titrimetry is the way forward, isn’t it?

Autotitrators are now commonly available in many laboratories and should be used to mitigate the manual operational risks of analyst systematic error. For the purpose of this article, it is assumed that management is convinced by the argument for automated titrimetry and has thus condoned the purchase of an autotitator.

 can be redrawn to produce an automated version, which is now shown in 

. As the concept of uncertainty is discussed later in this article using International Organization for Standardization error budget concepts, a new term must be introduced here to denote the output from an analytical process; that term is the measurand (1). This term simply means the particular quantity to be measured; in this instance, the quantity to be measured is the C

 Basic process flow for the automated titration.

Assuming that the instrument has been properly qualified, the titration process can be automated to minimize the titration procedural errors. Note that the word used here is “minimize” not “remove.” The process now relies on an autotitator with a digitally controlled piston burette under software control. As will be demonstrated, the use of this autotitrator makes the estimation of uncertainty in the measurand a straightforward process. This particular process was chosen because it was used as a worked example, A2, in the Quantifying Uncertainty in Analytical Measurement (QUAM:2012.P1) (2) for the classical error budget approach that will be described later.

Having drawn the automated process flow, an Ishikawa (Fishbone) diagram can be used in 

 to examine in more detail the four process elements that contribute to the uncertainty, namely:

the molecular weight of the KHP CRM itself

the volume of the NaOH titrant required to neutralize the weight of the KHP CRM.

 Ishikawa diagram for the automated titration and the measurand. KHP is potassium hydrogen phthalate. CRM is certified reference material. NaOH is sodium hydroxide.

As this is an exercise, the uncertainties in the atomic masses of carbon, hydrogen, oxygen, and potassium will be considered, and the uncertainty of the molecular weight of KHP will be calculated. As will become apparent, this error source would not need to be considered routinely; however, without these data, there will be no basis for ignoring the error source in the future. With the setup in place, it will now be possible to estimate the uncertainty in the measurand. An error budget must first be constructed from the Ishikawa diagram, however.

The basics of the measurement uncertainty approach and error budgets have been described in previous papers (3,4), and the detailed formal calculation approach is described in another paper (2). However, it is best to visualize the components as a structured model as shown in 

, the reason being that the visualization makes clear the interrelationships between the error sources needed when performing the calculations. 

 Error budget structure for the automated titration. NaOH is sodium hydroxide. SD is standard deviation. KHP is potassium hydrogen phthalate.

There are two distinct calculation methods available. The first method requires approximations of the error distributions and an Excel spreadsheet to give a single value as described in the EURACHEM/CITAC Guide (2), and the second method does not require approximations and produces a series distribution of values; the second method does, however, require simulation software (5). The starting point is the same for both these methods.

The equations to be used are facilitated if 

 is updated to reflect the autotitator process and the uncertainty contribution data are added, as shown in 

 Error source mapping and process flow for the automated titration with uncertainty data. KHP is potassium hydrogen phthalate. NaOH is sodium hydroxide.

For example, the corrected volume of the titrant, V

, is a function of the accuracy of the autotitator, the temperature of the laboratory, and the measured volume. The accuracy of the autotitator is known to be ±0.03 mL. It is assumed that this range is distributed as a triangular distribution because it is more likely to have a value nearer the center.

The laboratory temperature is controlled between 17 °C and 23 °C, a range that has a uniform or rectangular distribution. In the first calculation approach (2), these ranges are converted to standard deviations, using approximations, by dividing the values using factors of √6 and √3. The basis for these approximation factors is described in a technical appendix (2).

The overall uncertainty of the measurand using the method fully described in the EURACHEM/CITAC Guide (2) is given as 

This equation may be mathematically elegant, but it is time consuming to carry out and not really user friendly for use in laboratories.

However, there is another, more visual way. The second method, which uses Monte Carlo Simulation (MCS), will be described in more detail later in this article, where a comparison of the results from these two approaches will be given. The classical calculation, illustrated in 

, gives a single value of the uncertainties and approximates the distributions. MCS yields many values that are then used to calculate the required values and associated uncertainties. This method is much quicker and easier to perform but requires large amounts of computing power and a specialist software application.

MCS statistical modeling has been around for more than 70 years (6), but because it requires large amounts of computational power, it remained a specialist area for approximately 60 years or so. With the advent of cloud computing and user-friendly application software, however, MCS modeling is now readily achievable without the need for programming knowledge. In this paper, Minitab Workspace software (5) is used for the automated titration example.

Having identified 12 inputs (X) for the error budget, one can set up the contributions using drop down menus for distributions from 

 and then write the output equations. The 12 inputs are shown in 

. The distribution type can be a fixed value (constant) as in the case of the observed mass of KHP and the observed volume of titrant. Visually, the distribution shape is automatically given based on the selected distribution. 

 Inputs for the Monte Carlo Simulation of the automated titration. KHP is potassium hydrogen phthalate. NaOH is sodium hydroxide.

Based on these inputs, simple equations can be written for the five outputs (Y) needed to calculate the uncertainty of the measurand only using the X input variables already defined in 

 Output equations required for the Monte Carlo Simulation of the automated titration (note that the temperature correction for volume name is truncated in the display). KHP is potassium hydrogen phthalate. NaOH is sodium hydroxide.

Before the simulation is run, there is a dendrogram display of the relationships between the 12 (X) inputs in blue and the five (Y) output equations in orange, which should be checked for model correctness and for completeness, as shown in 

Monte Carlo Simulation model relationships for the automated titration. NaOH is sodium hydroxide. KHP is potassium hydrogen phthalate.

The purity of the KHP input has been included in the mass of KHP output, and the RFP factor has been included in the volume output for computational convenience; these inputs yield the same numerical answers. However, the structure in 

 is necessary when the comparison of the two approaches is considered.

Now the MCS model can be run. How many iterations (N) are needed to get reliable outputs?Usually, 10 to 50 thousand iterations are sufficient; however, Minitab Workspace currently allows up to one million (note that this would not be practical in Excel).

N = 500,000 was selected for this example. How long does it take to do the calculations half a million times, do the statistics, and plot the output files in the Cloud? The answer to that question is not clear because much of the time depends on the speed of the Internet connection. For this example, the time taken was about 10 seconds, which was quick enough, and another few seconds to download and export the file to PowerPoint. The total time to set up the X inputs and Y outputs was approximately 30 minutes. The output (see 

) gave a value for the measurand of 0.1021, which was close to that of the manual value of 0.1019. When a 95% confidence range is calculated from the modeling, the results come out to 0.1019–0.1023 by both approaches. 

 Measurand output from Minitab Workspace. NaOH is sodium hydroxide.

Before looking at a comparison between the two approaches in more detail, it is interesting to take note of the uncertainty of the molecular weight of the KHP, while keeping in mind that the atomic masses of carbon, hydrogen, oxygen, and potassium from the EURACHEM/CITAC Guide were inputted, and that those atomic masses were taken from International Union of Pure and Applied Chemistry values at the time with their uncertainties model as a rectangular distribution. The molecular weight of KHP, as defined by the first Y output equation in 

, was calculated 500,000 times from values selected at random from the defined rectangular distributions.The result is shown in 

 Molecular weight for potassium hydrogen phthalate (KHP) from the Monte Carlo Simulation model.

Therefore, the relative standard deviation in the molecular weight of KHP due to uncertainties in the atomic masses is 0.0019%. Now that this value is known, it won’t be necessary to include it next time.

The results from the classical QUAM calculation approach and the MCS approach are shown in 

. Whilst there are some small individual differences, which would be expected, the uncertainty values for the measurand are comfortingly close. However, the real power of using a modeling approach is the comparison of the sizes of the error sources. This comparison is graphically shown in 

 Numerical results from the two measurement uncertainty calculation approaches. MCS is Monte Carlo Simulation. KHP is potassium hydrogen phthalate. NaOH is sodium hydroxide.

 Error contributions as calculated by the two approaches.

Pictures speak louder than words! Both approaches are comparable and provide the same information: having gotten rid of the analyst contributions, the remainder is dominated by the autotitator contributions for accuracy and precision of the titrant volume. At 95% confidence, the uncertainty for the molar concentration of the NaOH solution is 0.20%, which is, for most purposes, good enough.

Analytical chemists need to be comfortable with the error structure of their procedures and need to be able to communicate measurement uncertainty to management. To do this effectively and efficiently, they should consider employing the tools of process mapping and error budget calculation approaches as described.Data integrity and data quality are increasingly subject to regulatory scrutiny, and scientifically sound and defendable approaches are thus required.

International Vocabulary of Metrology—Basic and General Concepts and Associated Terms (VIM)

Quantifying Uncertainty in Analytical Measurement

.
3. Burgess, C. The Basics of Measurement Uncertainty in Pharma Analysis. 

 (9).
4. Burgess, C. Measurement Uncertainty without the Math. 

 (2), pp. 36–40.
5. Minitab. Minitab Workspace. www.minitab.com (accessed on Sept. 14, 2022).
6. Metropolis, N.; Ulam, S. The Monte Carlo Method

Articles

How confident are you in your lab results? This paper explores this question and, by way of a worked example, provides a methodology to answer it.

Never Mind the Statistics; Just Tell Me What the Answer Is! 

Polyplus, a French provider of solutions for advanced biologic and cell and gene therapy production, announced a novel cationic lipid nanoparticle (LNP) formulation for messenger RNA (mRNA) therapeutic and vaccine development on March 14, 2023. Set for release in April 2023, the LipidBrick IM21.7c is a formulation designed to expand the scope of LNP usage in various therapeutic and oncology applications, as well as prophylactic vaccines.

The formulation is designed to overcome current-generation ionizable lipid challenges used in current LNPs by enabling a wider 

biodistribution and decreasing accumulation in the liver. According to a company press release, proof of concept work demonstrated that it successfully modulates the overall charge of LNPs, improving drug efficiency and formulation stability with no additional safety concerns.

“Polyplus draws on over 20 years legacy in cationic polymer and lipid-based delivery systems for both 

 models to power mRNA therapeutics,” said Mario Philips, CEO, Polyplus, in the release. “As we carry forward this legacy, we are working to ensure that our offer opens new possibilities to improve LNP formulation to meet the growing demand of the mRNA therapeutic market. LipidBrick IM21.7c is already used in jetMESSENGER and liposomal formulations for mRNA delivery, as a ready-to-use solution that requires no onsite formulation.”

Polyplus’ LipidBrick is designed to expand the scope of lipid nanoparticle formulation usage in mRNA therapeutics and vaccine development.

Polyplus Introduces Novel LNP Formulation for Use in mRNA Therapeutics

Stevanato Group, an Italian provider of drug containment, drug delivery, and diagnostic solutions, announced a collaboration with Recipharm, a Swedish contract development and manufacturing organization, on March 16, 2023. Under the agreement, Stevanato will support development and production of pre-fillable syringes for use in Recipharm’s soft mist inhalers.

Stevanato will provide and manufacture its proprietary glass pre-fillable syringe, Alba, assembled with the integrated spray module of Recipharm’s soft mist inhaler technology, the Pre-Filled Syringe Inhaler (PSFI). According to a company press release, the goal of this partnership is to deliver drug products more efficiently to the respiratory airways, as well as provide biopharma companies with a containment solution that features enhanced stability and safety.

“By leveraging Stevanato Group's integrated capabilities, from plastic injection molding and assembly capabilities to comprehensive scientific and analytical support services, we will be able to offer a turnkey solution, and accelerate and de-risk our pharmaceutical customers’ development programs,” said Bernhard Muellinger, general manager and chief operational officer, Resyca (a joint venture between Recipharm and Medspray), in the release. “This is particularly true for novel inhaled biological products.”

“Biopharma companies are advancing patient care with new, innovative treatments, particularly in biologics and mRNA [messenger RNA] therapies,” said Mauro Stocchi, chief business officer, Stevanto Group, in the release. “These products require specialized, high-performance drug containment systems, like our Alba syringe platform, together with patient-centric drug delivery devices [such as] the PFSI.”

Stevanato Group will support development and production of pre-fillable syringes for use in Recipharm’s soft mist inhalers.

Stevanato Group and Recipharm to Collaborate on Pre-fillable Syringes for Soft Mist Inhaler

Evotec announced on March 16, 2023 that there has been progress within its strategic partnership with Bristol Myers Squibb (BMS) relating to building a molecular glue-based pipeline. Performance-based and program-based achievements will total $75 million to Evotec.

The companies entered their strategic protein degradation partnership in 2018 and expanded it in May 2022. Evotec has scaled up its activities to develop compounds from Bristol Myers Squibb’s library of cereblon E3 ligase modulators since the expansion. The partnership’s goal is to develop high-value targets in the field of oncology and more by building a pipeline of novel molecular glue degraders. According to a company press release, molecular glue degraders are compounds that induce interactions between an E3 ubiquitin ligase and a molecular target, which results in ubiquitination and subsequent degradation of the protein. The molecular glue itself is not degraded in the process, meaning it can trigger the degradation process several times over, which can lead to longer-lasting therapeutic effects.

“The recent extension and expansion of our partnership with BMS continues to deliver high value programs,” said Cord Dohrmann, chief scientific officer of Evotec, in a company press release. “Our Omics-based, highly systematic, and industrialized approach to identify very promising molecular glues continually exceeds all expectations and will translate into an extraordinary pipeline of first-in-class product opportunities.”

Evotec and Bristol Myers Squibb have announced progress in their strategic protein degradation partnership. 

Evotec and Bristol Myers Squibb Progress Strategic Partnership

Cancer Research Horizons announced on March 17, 2023 that it has partnered with Predictive Oncology to drive the development of oncology drugs. The collaboration will use Predictive Oncology’s PEDAL artificial intelligence and machine learning platform technology to determine which cancer types and patient populations are most likely to respond to treatment with Cancer Research Horizon’s pre-clinical drug inhibitors of glutaminase. By addressing tumor heterogeneity during pre-clinical stages, the probability of clinical success increases, according to Predictive Oncology.

The agreement joins together non-profit and industry and offers the potential for Cancer Research Horizons to incorporate PEDAL technology into future drug discovery processes.

“Our collaboration with Cancer Research Horizons represents a fundamental shift in drug discovery that could lead to the accelerated development of cancer therapies using compounds originating from the CRUK network and developed by Cancer Research Horizons in partnership with their global network,” said Raymond F. Vennare, CEO of Predictive Oncology, in a company press release. “We now have the ability to impact the most critical stage of early drug discovery in a way that has never been done before.”

“We are committed to working closely with scientists to discover and advance therapies that can help in the fight against cancer. Ensuring that patients receive the right treatment for them is of paramount importance and is why we are excited to work with Predictive Oncology to identify the most suitable population for the further development of our compounds. We look forward to applying Predictive Oncology’s expertise, enabling technology and dedication to progress exciting cancer therapeutics,” explained Tony Hickson, chief business officer for Cancer Research UK and Cancer Research Horizons, in the release. “We consider the potential of this collaboration a win for patients not just in the UK or US, but worldwide.”

Predictive Oncology and Cancer Research Horizons have partnered to pursue development of cancer drugs utilizing the PEDAL platform.

Predictive Oncology and Cancer Research Horizons Partner for Cancer Drug Development

Feliza Mirasol is the science editor for 

Sandoz, the generics and biosimilars division of Novartis, announced on March 9, 2023 that it has signed a memorandum of understanding to build a new biologics production plant in Lendava, Slovenia. The company expects to invest at least $400 million in this project, which represents one of the largest international private-sector investments in Slovenia, according to a company press release.

Work on the new plant is set to begin in 2023, and full operations are provisionally planned for late 2026. The facility will support the increasing global demand for biosimilars.

“Biosimilar medicines increase access to cutting-edge biologic therapies for the patients who need them most. This investment underscores our ambition to be the sustainable global leader in biosimilars, a segment projected to grow double-digit annually over the next decade,” said Richard Saynor, CEO, Sandoz, in the press release.

“This state-of-the-art site will … enable[e] us to meet growing demand for our current and future biosimilars in the mid- to long-term. The location offers us a strong combination of political stability, proximity to our existing European-based production and commercial operations, and competitive costs,” said Glenn Gerecke, global head of Technical Operations, Sandoz, in the release.

In addition to this biologics production facility, Sandoz also 

recently announced in 2022 an additional €50 million (US$53 million) investment

 (1) to expand its European-based antibiotics production network. Altogether, the company’s total new investment commitment in the past few years to expand its European-based antibiotics production network comes to €250 million (US$266 million).

1. Sandoz, “Sandoz Announces Further Investment in Key Manufacturing Facility in Austria, to Support Increased Global Demand for Essential Antibiotics,” Press Release, Nov. 7, 2022.

Sandoz, a Novartis division, is expected to invest at least $400 million to build a new biologics manufacturing facility in Lendava, Slovenia, to support growing demand for biosimilars.

Sandoz to Build New Biologics Production Facility in Slovenia

On March 16, 2023, Catalent announced that it has signed a licensing agreement with India-based Bhami Research Laboratory (BRL) that will provide Catalent with access to BRL’s formulation technology. Using the technology, Catalent intends to develop formulations that will help enable the subcutaneous delivery of high-concentration biologics. Financial terms of the agreement were not disclosed.

The agreement authorizes Catalent to collaborate with its customers to evaluate BRL’s formulation technology so that Catalent and its customers can explore ways to reduce viscosity and deliver high-concentration biologic products. BRL’s technology is applicable to a wide range of monoclonal antibodies and fusion proteins. Successful programs could be integrated into Catalent’s formulation and manufacturing services at scale.

According to a Catalent press release, the subcutaneous delivery of small volume, low viscosity biotherapeutics can provide superior patient benefits over traditional intravenous injections. Such benefits include improved access to healthcare, reduced need for hospitalization and overall treatment cost, and the opportunity to employ new delivery technologies, such as auto-injectors.

“The patented, high-concentration, low-viscosity protein drug delivery platform has been designed to make patient care convenient, affordable, and safe,” said Bhami Shenoy, co-founder and chief scientific officer of BRL, in the press release.

“This agreement allows Catalent Biologics to offer its customers a novel patented formulation that may be evaluated for enabling the delivery of high-concentration therapies by reducing viscosity,” said Julien Meissonnier, Catalent’s chief scientific officer, in the release. “The potential for maintaining the stability of a drug with the convenience of a pre-filled syringe or auto-injector could have a significant impact on reducing the barriers to subcutaneous delivery of therapies and improving the patient experience.”

Under a technology licensing agreement with Bhami Research Laboratory, Catalent intends to develop formulations that will allow for the delivery of high-concentration biologics subcutaneously.

Catalent Licenses Bhami Research Laboratory Technology for Biologics Formulation Development

Genuv, a South Korean-based clinical-stage biotechnology company, announced on March 15, 2023 that it has entered into a partnership with South Korea-based biopharmaceutical company Celltrion to jointly discover and develop novel therapeutic antibodies. Under the agreement, the companies will use Genuv's proprietary SHINE MOUSE platform.

According to the agreement, Genuv will conduct antibody discovery services using its mouse platform technology. Drug discovery will be followed by a research collaboration. Celltrion has the option for joint research and development projects that, if exercised, will trigger agreed-upon milestones, which include development milestones of up to $25 million followed by total commercial milestones of up to $680 million. The latter amount will be triggered dependent upon the accumulation of $7.5 billion in sales per candidate antibody.

The advantage that the SHINE MOUSE platform offers rests in the platform’s ability to generate a greater diversity of antibodies than conventional mice can generate. This ability led to the discovery of the experimental, novel anti-programmed cell death protein 1 monoclonal antibody, GNUV201. GNUV201 holds unique features, such as exceptional interspecies cross-reactivity and binding affinity, which allows for preclinical disease animal model studies that have increased clinical relevance, according to a company press release.

Genuv is also developing GNUV205, an improved interleukin-2-based immunocytokine anticancer drug that is made tumor-specific by leveraging the properties of GNUV201.

“We are excited to embark on our mouse platform business with Celltrion as our first partner,” said Sungho Han, founder and CEO of Genuv, in the press release. “We believe Celltrion's in-depth knowledge and experiences in antibody development and production and Genuv's expertise in novel antibody discovery with our SHINE MOUSE platform could generate great synergies in providing innovative antibody therapeutics.”

“Celltrion is continuously interested in antibody-based new drugs and anticipates to discover differentiated antibodies through innovative antibody discovery platform technology. We look forward to partnering with Genuv for the development of innovative antibody therapeutics that will bring new hope to patients with high unmet needs,” said Kisung Kwon, head of the R&D unit of Celltrion, in the release.

The collaboration between Genuv and Celltrion will utilize Genuv's proprietary mouse platform for antibody discovery.

Genuv and Celltrion Partner on Development of Novel Therapeutic Antibodies

Pfizer and Seagen, a global biotechnology company specializing in antibody-drug conjugates (ADC), announced a definitive merger agreement on March 13, 2023. The deal will see Pfizer acquire all outstanding shares of Seagen for a total of $43 billion. The companies expect to complete the transaction in late 2023 or early 2024, subject to approval of Seagen’s stockholders and receipt of required regulatory approvals.

According to a company press release, Seagen’s portfolio includes four approved medicines that are first- or best-in-class in their respective indications across solid tumors and hematologic malignancies: three of these, Adcetris (brentuximab vedotin), Padcev (enfortumab vedotin), and Tivdak (tisotumab vedotin), are ADCs. The company’s technology is used in four of the twelve total FDA-approved and marketed ADCs.

In the release, Seagen stated that it expects to generate approximately $2.2 billion of revenue in 2023, representing 12% growth relative to 2022. Additionally, Pfizer indicated it believed that Seagen could contribute more than $10 billion in risk-adjusted revenues by 2030, as well as provide potential significant growth beyond that.

“Pfizer is deploying its financial resources to advance the battle against cancer, a leading cause of death worldwide with a significant impact on public health,” said Albert Bourla, chairman and CEO, in the release. “Together, Pfizer and Seagen seek to accelerate the next generation of cancer breakthroughs and bring new solutions to patients by combining the power of Seagen’s ADC technology with the scale and strength of Pfizer’s capabilities and expertise. Oncology continues to be the largest growth driver in global medicine, and this acquisition will enhance Pfizer’s position in this important space and contribute meaningfully to the achievement of Pfizer’s near- and long-term financial goals.”

“Pfizer shares our steadfast commitment to patients, and this combination is a testament to the passion, dedication and talent of the Seagen team to achieve our mission to discover, develop, and commercialize transformative cancer medicines that make a meaningful difference in people’s lives,” said David Epstein, CEO, Seagen, in the release. “The proposed combination with Pfizer is the right next step for Seagen to further its strategy, and this compelling transaction will deliver significant and immediate value to our stockholders and provide new opportunities for our colleagues as part of a larger science-driven, patient-centric, global company.”

Pfizer will acquire all outstanding shares of Seagen for a total of $43 billion.