Cost and supply considerations for antibody therapeutics

ABSTRACT

Monoclonal antibodies (mAbs) and mAb-derived biologics have achieved substantial success across various therapeutic areas over recent decades. Their widespread adoption, however, remains constrained due to high prices and challenges in supply. Here, we examine the general price and cost structure of mAbs and mAb-derived therapeutics and identify directions to improve affordability and strategies to ensure supply. Mainstream and emerging biomanufacturing formats and their implications on cost and supply are discussed. We also summarize modeling tools used across industry for process economics analysis, emphasizing the importance of this assessment throughout the product development lifecycle. A comprehensive understanding of cost and supply scenarios will empower industry players to thrive despite competition, navigate supply challenges, and broaden access to mAb therapeutics for more patients.

Introduction

Since the introduction of Orthoclone OKT3® (muromonab-CD3) in 1986, monoclonal antibodies (mAbs) and mAb-derived biologics have demonstrated great success across various challenging therapeutic areas,^1,2 including cancers, immune disorders, and infection by agents such as human immunodeficiency viruses, respiratory syncytial virus, and severe acute respiratory syndrome coronavirus 2.^3,4 In parallel with this, the annual mAb market size has grown to $210 billion in 2022, and is projected to maintain an upward trajectory with a compound annual growth rate of 11% from 2023 to 2030.⁵ As the market continues to expand, healthcare benefits provided by antibodies are anticipated to be broader and more profound.

This expanding industry is not without its hurdles. In the decades since Orthoclone was approved, mAb therapeutics have remained expensive, limiting their widespread applications, especially in low-and-middle income countries (LMICs). According to an analysis of products approved by the US Food and Drug Administration (FDA) during 1997–2016, annual median prices for mAb or mAb-derived therapeutics vary between $15,624 and $143,833.⁶ When converted into price per gram of drug substance (DS) used in the treatment, it ranges from $4,650 per gram to a remarkable $114,318,850 per gram.⁶ As a more recent example at the low-price end, a mAb treatment for COVID-19 was mass procured by the US government at $2,100/dose,⁷ equivalent to ~$2,000 per gram. Oftentimes, the high price of mAbs is attributed to substantial costs associated with development and manufacturing processes. Research and development (R&D) for new drug is indeed expensive, typically ranging from $1–2 billion per approved product.^8–10 However, R&D spend alone does not explain the higher price of mAb therapeutics versus small molecule therapeutics, since there is no substantial R&D spend difference between biologics and small molecules.^10,11 On the other hand, over the past few decades, cost of goods (COGs) for manufacturing mAbs has decreased significantly from ~$1,000s to $10s-$100s per gram due to improved manufacturing technology.^12–16 A gap between the price of ~$2,000s per gram (or higher) for mAb therapeutics and the current best-practice COGs $10s-$100s per gram implies a huge potential for mAb price reduction. Unlocking this potential requires careful examinations of all cost drivers and their contributions, including but not limited to, R&D spend and COGs. As the biosimilar market becomes increasingly competitive, along with rising demand for cost-effective antibodies for chronic and infectious diseases,^17–19 a clear understanding of the price structure will help industry players to identify directions to reduce the cost and price, stay competitive, and make mAbs affordable to more patients.

Supplying the dynamic demand and diversifying product portfolio with adequate biomanufacturing capacity is another challenge for the biopharmaceutical industry. Currently, the majority of mAbs are manufactured through cell culture process in bioreactors using mammalian cells, predominantly Chinese hamster ovary (CHO) cells. By 2022, the total volume of bioreactors suitable for mammalian cell culture reached > 6 million liters worldwide, a dramatic increase from ~0.8 million liters in 2002.²⁰ Globally, ~30 metric tons of mAbs and mAb-based biologics were produced in 2020 to meet commercial needs.²¹ It is projected the annual demand for mAb therapeutics may increase to ~100s of tons if the potential of antibodies is fully unlocked to supply the rising demand for cost-effective, high-need mAb treatments, such as those for infectious diseases.¹⁷ If assuming ~6 million liters capacity is utilized to cover ~100 tons annual demand, each liter of bioreactor would only need to produce <17 g per year or <0.05 g per day. This is on the low end, even considering legacy, low productivity products. Therefore, across industry, more than enough capacity may already exist. However, the capacity situation could differ substantially among individual biotech firms, with some struggling with under-capacity and the possibility of supply shortages, and some over-capacity, which could lead to wasted investment, high cost, and even a financial crisis. For individual biotech firms, properly balancing the rising and diversifying demand with adequate biomanufacturing capacity is a critical task. However, building or expanding in-house capacity at the right time, with appropriate scale and technology is complex. It is also risky because substantial capital investment is required years in advance, despite huge uncertainties on demand and regulatory approval. Contract Manufacturing Organizations (CMOs) have recently risen as a popular solution, offering capacity flexibility and risk mitigation for individual companies.²² Since 2002, the proportion of mammalian-based capacity owned by CMOs increased from 7% to 22% in 2022.²⁰ Another industry trend is the growing adoption of single-use technologies and modular, configurable facility designs, which offer advantages such as smaller footprints, lower capital investment, enhanced configurability, and flexibility.²³ Single-use technologies enable rapid capacity scaling to meet sudden surges in demand, such as those witnessed during the COVID-19 pandemic, and facilitate regional manufacturing in alignment with regulatory, policy, and business requirements. Between 2018 and 2023, only ~20% of newly installed bioreactors were >10 kL scale traditional stainless-steel bioreactors.²¹ Despite the growing adoption of single-use bioreactors (SUBs), large-scale stainless-steel bioreactors remain the primary contributors to overall biomanufacturing capacity. Recent expansions, such as Samsung’s Plant 5, Fujifilm’s new facilities in Hillerød (Denmark) and North Carolina (United States), and Lonza’s acquisition of the world’s largest CHO facility in Vacaville (United States) from Roche, underscore the continued dominance of large-scale bioreactors in terms of available working volume. SUBs, in contrast, provide speed, flexibility, and adaptability, particularly for meeting diverse demands or supporting products at different development stages. Overall, industry’s growing reliance on CMOs and single-use technologies reflects a strategy to balance the capacity advantage of stainless-steel plants with the agility needed to manage varying production requirements.

In summary, cost reduction and capacity management are two challenges that the biopharmaceutical industry face as they develop and commercialize mAb therapeutics. In many cases, cost and/or supply assessment is necessary to support critical business decisions, such as candidate viability, manufacturing format and site selection, capacity expansion, and pricing. Individual firms usually adopt diverse approaches and assumptions, depending on their unique business needs and priorities, existing platforms, capabilities, and program development stages. Data pertaining to cost and supply are often proprietary and isolated within individual firms, creating barriers for open dialogs. Here, we aimed to survey the general price and cost structure of mAb therapeutics and set a common framework for industry players to examine key factors and opportunities to reduce cost and enhance affordability. We summarize and compare current mainstream and emerging manufacturing formats for mAb therapeutics and their implications on cost and supply, emphasizing the importance of a rational cost and supply analysis to support critical decision such as manufacturing format selection. Additionally, we survey commonly used tools and methods supporting cost and supply analysis, especially tools for process economic assessment that are suitable for mAb COGs modeling. Our results enable an aligned understanding of the importance of cost and supply in mAb therapeutics development, which, along with the ability to properly assess risks and opportunities associated at different development stages, is crucial for industrial players to thrive in intensifying competition, overcome the supply challenges, and benefit more patients.

Price and cost structure

‘High price’ and ‘high cost’ are oftentimes referred to interchangeably when discussing economics of mAbs. In fact, they have quite different meanings. To be precise, ‘price’ is usually defined as the net revenue gained from a sale of product, while ‘cost’ encompasses all the expenses incurred to deliver the product to the customers (patients). High cost could be one of the reasons for high price, but not necessarily. Numerous factors other than cost can influence the price of mAb therapeutics, including consumer willingness to pay, demand-supply dynamics, market competition, a company’s marketing and revenue recovery strategies, reimbursement frameworks, payer policies, and a range of other societal and organizational considerations.²⁴ Due to these multifaceted factors, prices for mAb therapeutics can vary significantly across distinct products, therapeutic areas, and marketing regions.^6,18,24

Despite huge variations in prices, all mAb therapeutics share a common price structure. Figure 1 provides a tree map to demonstrate the major contributing factors to the price of a typical or ‘average’ mAb product. R&D cost has often been cited as a major cause for high prices for mAbs and mAb-based biologics. Overall, the R&D expenditure across the pharmaceutical sector constituted 25% of net revenue in 2017, which is indeed higher than average 2–3% across all other industries.⁸ Interestingly, R&D expenditures for biologics do not seem to be significantly more than small molecule drugs.¹¹ More specifically, a recent report places R&D costs among Pharmaceutical Research and Manufacturers of America member companies, who focus more on biologics, in the 20–24% of revenue.²⁵ Within the R&D bracket, clinical studies cost contributes > 50% of the total R&D expense for a new drug.^8,26 Other components in the R&D expense category include preclinical and post-launch studies. The contribution of process development and manufacturing cost to overall R&D expenditure was estimated to be 13–17%.¹³ Furthermore, resources channeled into failed drugs often surpass those allocated to the successful one.⁸ The second major category in the price structure is the marketing cost, which consists of expenditures on sales forces, advertising, medical affairs, public relations, market data acquisition, and sometimes distribution-related activities. The exact proportion of the marketing cost to revenue varies for top industry players, but spending on sales and marketing is typically comparable to investment in R&D.²⁷ COGs for mAbs and mAb-based biologics manufacturing are the third major category in the price structure. Its contribution to the total price varies considerably, with some sources quoting figures as low as 1–5%,¹⁵ others suggesting 4–8%,²⁸ and some pegging the median at 19%.²⁹ For biosimilars, COGs can constitute a median of 25% of a drug’s price.²⁹ Other miscellaneous items include the opportunity cost for the capital, administrative and legal expenditures crucial for company operations and regulatory compliance. Finally, profit makes the most variable component in the price structure. Profit margin is intrinsically dynamic across products and time, influenced by the company’s pricing and profit-taking strategy and many other factors, including competition.³⁰

Figure 1.

Price and cost of goods structure for Biologics (tree maps). Left: price structure. Note that COGs portion residing in the price structure was illustrated by using the nature of different components. Right: COGs structure by manufacturing process unit operations. Note that the proportions of each individual contributing factors in both structures are for illustration purposes only for an ‘average’ or typical mAb product, and it can vary significantly per each specific biologic product.

Among all the components in the price structure for mAbs, COGs often stands out in discussion of price reduction potential, as there is a common perception that high COGs is one of the major causes limiting affordability of mAb therapeutics. COGs indeed sets the lower boundary for the price as it is not as elective as other factors in the price structure, e.g., profit margin, R&D spend recovery speed. Considerable progress has already been achieved in understanding and reducing COGs, but continued effort is still needed for industry players and global healthcare organizations aiming to further improve affordability of mAb therapeutics. The manufacturing processes for mAbs and mAb-based biologics have two critical stages, the drug substance (DS) process and the drug product (DP) process.³¹ DS process includes upstream processing, where recombinant mAb molecules are produced using living cells and harvested from bioreactors, and downstream processing, which includes chromatographic operations and filtrations to separate product from impurities. The DP process, which involves transforming the purified bulk drug substance into a usable format for the patient, can also be costly. Costs associated with label, packaging and distribution should also be considered as they are all incurred before delivering the drug to patients. In addition, quality control and assurance, as well as regulatory compliance across all stages of mAb manufacturing also take a sizable portion in the COGs. Among all these elements, only COGs associated with DS manufacturing process has been systematically studied.^32–36 In these studies, COGs of DS are further itemized into detailed unit operations, or by the nature of components. According to one study,³² for a DS process using traditional fed-batch mammalian cell culture process at the commercial stage, the cost from capital investment for facility and equipment, crucial for mAb production and storage, accounts for around 59% of the overall cost. Raw materials and consumables, on the other hand, make up 18%. Staffing and labor costs, key for all aspects of production and distribution, contribute around 15% of COGs. Lastly, the cost required for quality control and quality assurance (QC/QA) requires 8% of the COGs. Apparently, these breakdowns can vary significantly across products manufactured using varying processes and formats. Figure 1 provides a tree map to demonstrate cost drivers for COGs by unit operations (Figure 1, right panel) and by components for a typical mAb (Figure 1, COGs section in left panel).

The fact that COGs usually make up a small fraction of the overall price for most mAb biologics nowadays, and the cost associated with DS manufacturing does not represent the entire COGs, has two implications. First, it is essential to identify early if there is price pressure for the product under development. If not, extensive measures to cut COGs, or specifically DS cost, may be less valuable compared to the speed of bringing the product to the patient. Second, for products under price pressure, like biosimilars and those low-cost products developed to serve patients in LMICs, identifying and confirming strategy to lower costs associated with R&D and marketing, as well as other non-DS components in COGs (e.g., DP), might be just as impactful as cutting DS COGs, if not more. For example, for developing low-cost products to serve patients in LMICs, industry players might be able to engage public and private funding sources to cover part or full R&D cost, and they may compensate for lower profit in LMICs market by higher profit from high-income markets. Marketing cost could be minimized for products to serve LMICs because it does not require extensive investment in sales forces, advertising, and it may leverage existing supply chain network established by public health organizations for similar products. For most mAb therapeutics, DP processing contributes less to COGs than DS, but this depends on factors such as the required DS content per dose, the DP container type, and the specific manufacturing process. DP and fill-finish costs can dominate when the product has high potency, a low dose, an already optimized (low) DS COGs, or requires a costly device like an autoinjector. For highly potent products, even if DP costs become a larger fraction of total costs, the absolute cost often remains low enough to be attractive for LMICs, meaning further DP cost reductions may yield only marginal returns. Thus, a detailed, scenario-specific analysis of pricing and COGs, along with an understanding of each contributing factor, can help identify reasonable and effective strategies for cost and price reduction.

To enable access to affordable biologics in the future,¹⁸ efficiency in development, manufacturing, and marketing can all be incentivized, promoting a potential paradigm shift in the biotechnology industry. R&D costs can be lowered as the industry’s experience with mAbs and their applications continue to expand. Streamlined R&D platforms can also add efficiency. The use of data analytics, artificial intelligence (AI), and machine learning for drug discovery, design, development, and clinical trials provides further opportunities for improved success rate and R&D cost reduction.³⁷ At the same time, better candidates can be engineered and selected to reduce the dose and/or dosing frequency and thus reduce the cost needed for treatment.¹⁶ Sales and marketing costs can be controlled through strategic agreements among companies, payers, distributors, and policy makers, avoiding unnecessary marketing expenditures.³⁸ Beyond these, sharing resources and capacities through collaborations and partnerships can minimize repetitive investment.³⁹ Outsourcing certain functions in product development can improve efficiency.^40,41 Regulatory innovations, like FDA’s accelerated approval pathways, can also bring drugs to market more quickly and save cost.⁴² Opportunities for COGs reduction may include intensified DS process, optimized facility use, adoption of advanced technologies such as single-use and configurable systems, productivity enhancements through media and process optimization, use of cheaper raw material and labors, and use of alternative expression systems.^35,36 Finally, as the biopharmaceutical industry becomes increasingly competitive, profit margins are expected to compress, especially for those with expired patents allowing biosimilars.⁴³ These considerations, taken together, suggest a pathway to more affordable mAb therapeutics for improved patient access and health outcomes.

Biomanufacturing format

As cost and supply come under more scrutiny driven by the need for affordability and accessibility for mAbs and mAb-based biologics, biomanufacturing technologies and corresponding scaling up strategies have surfaced as opportunities for improvement. Intensified and/or continuous cell culture formats in DS process, in contrast to traditional batch or fed-batch cell culture format, has gained more popularity in recent years. Perfusion technology, historically used in the production of labile products, is now being considered for process intensification and cost saving.^44,45 However, its value on COGs reduction depends on many variables,^35,36 and its readiness and fit into the current biomanufacturing infrastructure are under debate.^46,47 Considerations around the use of process intensification technologies and new manufacturing formats include not solely cost, but also impacts on timelines, flexibility around space and capital investment, the ease of scale-up and process development, and regulatory implications.

Biomanufacturing of most mAb biologics still relies on traditional batch or fed-batch processes, valued for their straightforward operation and ease of development.^15,48 These processes, typically involving 12–15 days of bioreactor runs and an additional 3–5 days for purification, can be scaled up with the use of large stainless-steel tanks (>12,000 liter), thereby achieving economies of scale (Figure 2, Scale-up). Moreover, in the same facility, multiple bioreactors can be operated in a staggered schedule, delivering a batch in approximately every 3–5 days. If managed properly, it can be operated as a macroscopic, plant-level ‘continuous’ production and is exceptionally efficient. To further enhance productivity, intensified techniques, such as N-1 perfusion to boost inoculation density, and the use of low-rate perfusion to increase cell density have recently been integrated into the process.⁴⁵ Despite its strengths, this ‘scale-up’ strategy using large, stainless-steel reactors has limitations, including high upfront capital investment and long facility building times, and considerable operation costs for cleaning and sterilization.³⁴ These factors make the strategy less attractive, particularly for products with lower demand and smaller organizations, which desire more flexibility in space and less initial capital spending. However, large capital investments can be avoided by partnering with CMOs that already have high-capacity infrastructure in place. This option is often overlooked when comparing different biomanufacturing formats, but can significantly shift the economic balance in favor of traditional fed-batch processes.

Figure 2.

Biomanufacturing formats and scaling up strategies. A comparison of three DS manufacturing formats and strategies to scale up for commercial manufacturing utilizing three different dimensions (scale-up: increasing size of the bioreactor; scale-out: increasing number of the bioreactor; scale-by-time: extending time or duration of the bioreactor operation), considering the advantages and disadvantages of each of them regarding key factors such as capital investment requirement, facility build time, consumable cost, scale differences between development and commercial stages, and process complexity. Different symbols are used to denote each factor, and the number of symbols next to the format is used to denote how that factor adversely impacts the format. One symbol: low impact; two symbols: medium impact; three symbols: high impact.

A compelling new avenue in biomanufacturing is the ‘scale-out’ approach, made feasible by the use of single-use and process intensification technologies (Figure 2, Scale-out).⁴⁹ This approach diverges from the traditional format that uses large stainless-steel bioreactors, pivoting instead toward smaller bioreactors, e.g., 2 kL, where single-use and perfusion technologies are feasible. With high-rate perfusion techniques, these reactors could potentially achieve volumetric productivity approximating 1–2 g per liter per day (or higher), thereby offering a competitive mass output in a 2kL reactor equivalent to a traditional large-scale 12 kL reactor.⁴⁴ Recently, as single-use technology advances and the size of single-use bioreactor grows up to ~6000 L,⁵⁰ a new opportunity emerges. By coupling multiple single-use bioreactors running at non-intensified but less complicated fed-batch mode, with one downstream purification train, a batch size equivalent to the large stainless-steel bioreactors can be achieved. For commercialization, rather than scaling up from smaller to larger reactors to satisfy high commercial demand, facilities can simply scale out by adding identical, single-use reactors, mitigating risks associated with performance variation during scale up. This strategy, however, is not without its limitations. The heavy reliance on single-use consumables, the operation complexity and substantial volume of perfusion media, if perfusion is used, introduces cost implications. Moreover, for large-volume products, using a scale-out strategy with multiple single-use bioreactors or facilities may ultimately require more capital investment than using the scale-up strategy, which needs to be carefully evaluated in cost assessments.

The latest trend in biomanufacturing proposes a ‘scale-by-time’ strategy to further reduce manufacturing footprint and facility-related cost, leveraging perfusion technologies and prolonged cell line stability to extend the duration of cell culture process to more than 2 weeks (Figure 2, Scale-by-time).⁴⁴ This approach minimizes downtime of the facility, removes the ‘white space’ required for equipment turnaround between runs. It also seeks to integrate upstream, downstream processes and processes analytical technologies for efficiency and thus cost-saving.^51,52 However, the implementation of this strategy for early-stage biologics programs may be time consuming and challenging. One possible approach could be an initial short-duration perfusion process, around 15 days, to enable Phase 1 clinical studies. This then provides an option for commercial process development to extend the culture duration longer. However, the batch sizes achievable through this format may not be comparable to the traditional format, especially for products of lower productivity. In high-demand scenarios, the accumulated overheads of maintaining additional equipment, enabling automation, and increased batch-specific QA/QC costs may offset the cost effectiveness of this approach. A recent report suggests that unless the volumetric productivity surpasses ~3 g/L-day, the relatively smaller batch size could incur overhead costs that undermine the overall cost-effectiveness compared to traditional approaches.⁵³ In addition, the operation complexity, time and resources needed for process development, and risk associated with regulatory acceptance on new approaches could offset the benefit of this format in flexibility.^46,54

In response to the cost pressure and supply challenge for mAb therapeutics, the biomanufacturing industry, including both biotechnology firms and CMOs, are expanding their capacity and also diversifying their supply strategies.¹⁴ Oftentimes, they may invest in evaluating multiple biomanufacturing formats, but only prioritize one for large scale implementation based on their strategy and needs. For example, among CMOs, companies like Samsung Biologics continue leveraging large stainless-steel bioreactors for commercial manufacturing to achieve economics of scales,⁵⁵ while firms like Wuxi Biologics are starting to adopt single-use technologies and the ‘scale-out’ strategy more for flexibility and efficiency.⁵⁶ Other emerging CMOs, such as JUST-Evotec, are focusing on the ‘scale-by-time’ strategy, with an emphasis on developing an integrated continuous manufacturing platform for cost-efficiency and flexibility.⁵³ To understand which approach or culture format best suits individual company and product needs, a detailed analysis to compare available options using suitable modeling tools is recommended to choose the right format and CMO partner, effectively balancing cost, supply, and other development needs.

Process economics modeling

Numerous process economic modeling tools suitable for mAb therapeutics have been previously reviewed.^57,58 Among them, BioSolve (Biopharm Services, UK), SuperPro Designer (Intelligen, NJ), and Aspen Batch Process Developer (Aspen Technology, MA) are commonly reported. BioSolve, introduced in 2008, is an Excel-based process simulator that incorporates a comprehensive range of predefined unit operation and process modes.⁵⁹ It features a routinely updated, industry-relevant cost database encompassing facility, equipment, consumables, and labor. BioSolve’s COGs analysis typically includes cost driver breakdowns by unit operation (e.g., upstream, downstream, release testing) or component nature (e.g., raw material, labor, facility). It allows user customization of process and cost data sets, making it suitable for economic analysis of new processes benchmarked against existing platforms, such as continuous versus batch manufacturing formats.⁵⁸ Outputs from BioSolve, including capital and throughput, can be utilized for net present value/cost and cash-flow analysis. However, BioSolve is a static analysis tool that does not currently support dynamic run scheduling, a function crucial for accurate economics and throughput analysis. SuperPro Designer is a flowsheet-based tool that takes a user-built mathematical model to derive equipment sizing, debottlenecking, cost, and economic evaluations.⁶⁰ It enables users to quantitatively analyze their existing manufacturing layout, avenues of scaling up or out, and identify cost-saving opportunities across batch or continuous processes. To achieve more advanced capabilities such as a Monte Carlo simulation, add-on packages and software can be used. As a recipe-based simulator, Aspen Batch is a similar tool. In addition to the standard economic and manufacturing analysis, it can conduct environmental impact analysis, estimating CO₂ emissions. With an added package, Aspen Batch is also able to evaluate dynamic processes, as opposed to the steady state estimations of other tools.⁶¹

The functionalities of some existing tools used for process economics analysis are summarized into a schematic diagram (Figure 3). Depending on what questions are to be addressed, numerous factors need to be taken as modeling inputs (Figure 3, left panel). For clarity, these contributing factors are categorized into three groups: facility-related, process-related, and product-related factors. Facility-related factors encompass everything related to facility that impacts process economics, including the facility type (e.g., using single use vs. stainless steel equipment), capital cost for building and equipment, depreciation schedules (usually over 20–30 years), and facility’s access to raw materials and labor. Additionally, facility performance parameters such as run rate, schedules, operation mode (e.g., single vs. multi-product), failure rate, and fault recovery mechanisms can impact a facility’s efficiency, subsequently impacting cost and throughput. Process-related factors include the process mode (batch, continuous, or hybrid), process durations (overall vs. individual unit operations), in-process control and release testing requirements, and the actual process definition itself. Process performance, such as bioreactor production titer, process yields, batch size, can also affect cost and supply. Product-related factors involve everything surrounding the product. One such factor is product demand, which can significantly affect the scale of manufacturing and thus COGs. Lower-demand products often cannot unlock economies of scale at manufacturing, potentially incurring higher batch fees due to smaller batch sizes or limited or no volume discount. The product demand can be influenced by dose regimen (e.g., dose level, frequency, and duration) and patient population across various product life cycle stages (e.g., initial launch, peak year), and the stock keeping unit (SKU) strategy (single-dose or multi-dose vial, vial excess content). Another product-related factor is the shelf life of the drug substance and product at its intended storage condition, which can impact the COGs associated with storage, delivery, as well as label-and-pack scheduling.

Figure 3.

Summary of existing process economic analysis tools. Column on left: modeling inputs, with yellow boxes representing facility-related inputs, blue boxes representing process-related inputs, and red boxes representing product-related inputs. Column in the middle: various modeling tools; column on right: outputs and functionalities of various modeling tools.

The exact subset of assumptions or factors to use for process economic assessment depends on the specific question to be addressed. COGs usually is the main question, but the need for process economic analysis often extends beyond that (Figure 3, right panel). For example, manufacturing throughput, supply risk analysis, profitability, environmental impact, process and facility optimization, and resource management, are often questions that arise during process economic assessment. To utilize process economics analysis to support critical business decisions, the tool itself sometimes is less important and some industry players may even implement and utilize their own tools. Instead, it is more vital to identify the right questions, use adequate assumptions, and acknowledge uncertainties with reasonable expectations across the different stages of product development and commercialization.

Economic analysis across product development life cycle

The development of mAb and mAb-based biologic products is a long and complex process that typically occurs in sequential stages, including discovery, candidate optimization and selection, process development enabling pre-clinical and clinical studies, commercialization, and lifecycle management. To ensure success, industry players usually use a risk-averse approach, implementing Target Product Profiles (TPPs) for continued viability assessments across the entire life cycle of product development. TPPs often include criteria such as safety, pharmacokinetics, and efficacy and sometimes desired Chemistry Manufacturing and Controls properties such as dosage forms. For products with price targets and/or supply pressures, COGs and supply requirements are beneficial to be included in TPPs from the initiation of a program.

Early and continued process economic analysis around COGs and supply is pivotal in ensuring viability of the candidate, guiding process development, and fostering rational decision-making throughout the product development lifecycle (Figure 4). For example, process economics analysis can be initiated as early as the molecule and manufacturability assessment phase. Despite limited information around facility, process, and the product itself at this stage, a ‘feasibility assessment’ can be achieved by setting up a base case using empirical knowledge and carrying out a scenario analysis to evaluate the potential best- and worst-case. This analysis can offer insights into the feasibility of the molecule, cell line, or process format to meet the price or supply goals as defined in TPP, before advancing to the more expensive next stage developments. As the development shifts to later stages, the economic analysis can be improved with more refined assumptions on variables such as process format, facility, productivity, dosage, SKU, and market demand. Since multiple factors interact with each other and contribute together, setting a threshold on a single factor, e.g., productivity, when performing such an analysis is not recommended. Instead, a multi-dimensional sensitivity analysis can be performed by scanning across an ‘uncertainty zone’, pointing out potential risks and opportunities. Coupled with process development, process economic analysis can enable cost-conscious development and aid critical process and facility decisions during commercial process development. Upon obtaining positive outcomes from clinical studies, the process economic analysis can be further updated with locked process and concrete process performance data, recommended dosage and SKU configurations, and more extensive market research data for demand projection. This improved model allows for a more accurate estimation prior to commercialization, supports cash flow and profitability evaluations, optimizes scheduling of process performance qualification campaigns, post approval inspection runs, and initial commercial launch runs, and informs pricing strategy. After the commercial launch, process economic analysis continues to be useful for optimizing manufacturing and supply scheduling, managing fault recovery, and informing resource management and optimization.

Figure 4.

Process economic analysis across biologics development lifecycle. Goals, key inputs, and example outputs & deliverables of the process economic analysis at different product development stages (pre-clinical, clinical and commercialization) are illustrated. The analysis and questions to be addressed for a specific program should be determined by the unique requirement of the individual product and its development stages.

It is important to understand that every company or project has unique needs, thus different process economic assessment strategies can be adopted. For instance, industry players relying on CMOs for commercial manufacturing may opt to perform a simplified analysis instead of a full scope, process modeling-based COGs analysis (Figure 5). They can leverage quotes received from CMOs, which should include information such as estimated batch size, batch fees, volume discount schedule, facility throughput, and estimated raw material cost, to perform a high-level analysis without the need for process details. While this approach may lack the granularity of understanding contributing factors for COGs, it is more relevant to those who do not have their own capacity, taking the actual contractual costs and CMO profit margins into consideration. However, if there is a need to build and utilize one’s own facility and/or seek further opportunities to reduce COGs, or when a sense check on batch fees and estimated raw material costs from CMO candidates is needed, it becomes critical and useful to perform a full scope analysis (Figure 5, Model 1 + Model 3). A combination of analysis using a real-world, quote-based approach and the detailed process modeling approach can offer valuable insights to guide the direction for process development and support critical process format and manufacturing partner decisions.

Figure 5.

Process economic analysis using full model or information from CMO. Model 1: DS process and facility modeling using DS process economic modeling tools such as BioSolve; Model 2: similar analysis performed by CMO supporting them on framing proposals for commercial manufacturing, which include information such as batch fee, material cost, volume discount, and throughput. Note that CMO may elect to derive this information based on experience instead of performing a detailed analysis using Model 2. Model 3: DP scenario modeling by using a tool (such as a spreadsheet) that is capable of scanning different cost factors for sensitivity analysis. Outputs (e.g., batch fee, batch size, material cost) from either Model 1 or Model 2 as well as additional product-related inputs will be taken as input for Model 3, and help to estimate cost per DP unit, or per dose, under different conditions. Model 1 + 3 is considered as full economic analysis typically performed by innovator firms who build their own manufacturing facility. Model 3 with information from CMO’s proposal (based on Model 2) is considered as a simplified analysis for firms relying on CMO’s manufacturing capacity.

As a case study of a low-COGs mAb product aimed at an infectious disease in LMICs, an example cost analysis under CMO settings is provided in Figure 6. Two manufacturing strategies (scale-up vs. scale-out) were analyzed and compared. Despite substantial dose and demand uncertainties at the time of analysis, one format demonstrates a clear advantage over the other, since it is more likely to hit the cost per dose target under the same dose, demand, and productivity assumptions. The analysis also shows how three factors with major uncertainties, including dose, demand, and productivity, interact together to impact cost, which helps identify opportunities to achieve the goal holistically. In addition, supply risk can be evaluated for both manufacturing formats by calculating the required capacity to meet peak demand. The proposed facility for both manufacturing formats can meet peak demand using available capacity, thus the risk of supply is low (analysis not shown).

Figure 6.

Example sensitivity analysis of cost comparing two proposed manufacturing formats. Format A and Format B: two potential manufacturing formats (or scaling up strategies) for commercialization. One is following ‘scale-up’ strategy using stainless steel bioreactors, the other is following ‘scale-out’ strategy using single-use bioreactors. Left: worst-case productivity scenario; middle: base-case productivity scenario; right: best-case productivity scenario. For each block in individual plot, color represents how estimated cost per dose compared to the cost per dose target. Red: not meeting target. Green: meeting target. Yellow: risk of not meeting target. X-axis of each plot: potential dose (mg/dose); Y-axis of each plot: potential annual demand in terms of million doses; circle in each plot indicating current best guess on dose and demand, which may shrink as development proceeds and more information become available. Dose, demand, and comparison of two scenarios in this analysis are for illustrative purposes only.

Summary

The past several decades has seen a marked uptick in the use of mAb biologics due to their notable success across a variety of therapeutic areas. The price of these treatments, however, remains high. Across the industry, challenges associated with cost and supply have limited the potential of mAbs and meeting the rising and diversifying demand may be difficult in the future. This is especially true for LMICs, where affordable and accessible mAb therapies do not exist, limiting the lifesaving potential of these therapies. It is also critical for industry players to remain efficient, agile and flexible, and survive in the more competitive industry. To provide a framework to understand the basis for the current high price of mAbs, we investigated the typical price structure for mAbs, outlining the major contributing factors in the overall price structure. We found the high price of mAb therapeutics cannot be simply attributed to high COGs, as it only consists of a portion of the overall price. This also explains why industry players were not incentivized enough to develop and adopt new manufacturing technologies in the past. However, COGs indeed set the lower boundary of the price. As other major price factors are being reduced or minimized through technical and/or business opportunities, contribution of COGs on price becomes more dominant for products such as biosimilars, and treatments for chronic disease and infectious diseases in LMICs. Currently, the main contributing factor to COGs is usually the DS cost. New DS manufacturing technologies and formats have emerged recently, not only for cost considerations but also for supply flexibility and development agility. By comparing traditional and emerging DS manufacturing formats, we conclude that the best manufacturing format will depend on the specific circumstances of the product being produced, and that leveraging process economic analysis tools to conduct cost and supply analysis is a necessary step to identify the best format for a specific program. We summarized several existing process economics analysis tools, drawing special attention to incorporating cost and supply analysis across the biologic’s development and manufacturing lifecycle. Which questions need to be answered will be determined by the development stage and the specific product or program requirement. In general, process economic analysis should be performed as early as the preclinical stage, focusing on feasibility despite great uncertainties. As more process, product, and facility details are known, the process economics analysis becomes finer tuned, which allows more specific questions at the various stages of the product development process to be answered.

Funding Statement

This work was supported by the Bill & Melinda Gates Foundation [Bill & Melinda Gates Medical Research Institute].

Disclosure statement

No potential conflict of interest was reported by the author(s).