Digital transformation is reshaping biopharmaceutical process development, which has traditionally been a time-intensive and costly endeavor. As the industry pivots from classical methods to innovative digital strategies, significant financial and operational benefits are emerging. In this adaptation of my MBA thesis “Assessing the economic impact of digital process development within the biopharmaceutical industry,” I discuss how advanced digital tools and strategies, including quality-by-design, hybrid modeling, transfer learning, and digital twins, not only enhance efficiency in process development but also substantially cut costs in manufacturing — achieving up to a 70% reduction in cost of goods sold (COGS).1 The strategic allocation of these gained efficiencies, requiring a corporate-wide and knowledge-based view, can lead to potential annual savings of $1.25 billion per blockbuster drug, by assuming a process development efficiency gain of 50%. This analysis underscores the transformative potential of digital process development, offering a blueprint for biopharmaceutical companies to optimize their manufacturing processes and secure a competitive edge in a rapidly evolving industry.
Biopharma Dynamics and Economic Impact
The biopharmaceutical sector plays a pivotal role in the global economy, contributing approximately $1.84 trillion, which equates to about 2.2% of the global GDP. Biopharmaceuticals, which include monoclonal antibodies, recombinant proteins, and antibody–drug conjugates, among other modalities, represent a significant portion of this sector, with a market size of $389.6 billion in 2021 that is projected to grow to $856.9 billion by 2030.2 Emerging therapies like mRNA vaccines and cell and gene therapies show considerable promise, expanding the sector’s potential despite complex manufacturing processes and rigorous regulatory hurdles.
A detailed financial analysis of the top pharmaceutical companies — Pfizer, AbbVie, Novartis, Johnson & Johnson, Roche, and Biogen — highlights the sector's operational and financial dynamics. Despite robust sales figures reaching up to $87 billion for Johnson & Johnson in average from 2018 to 2022, as an example, the industry faces diminishing returns on investment (ROI) in R&D. The average ROI on R&D dropped from 7.2% in 2014 to 1.6% in 2019, reflecting escalating costs and prolonged development timelines.3 The average cost to bring a biopharmaceutical product to market has surged to $3.1 billion, with R&D outlays reaching over $4 billion per successful market entry, highlighting the financial and operational strains of drug development.4
The life cycle of a biopharmaceutical product typically spans 12–13 years from discovery to regulatory approval. The process involves extensive R&D, with initial discovery phases focusing on identifying and optimizing potential drug candidates. Only the most promising molecules proceed to preclinical and clinical testing, where they must demonstrate safety and efficacy to regulatory bodies like the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA). However, the costs associated with unsuccessful candidates are considerable, highlighting the need for efficient and cost-effective development strategies to mitigate financial risks.
With increasing pressures from cost, competition, and regulatory challenges, the industry is urged to innovate to reduce R&D expenses and accelerate market entry. Digital transformation represents a crucial strategy in this endeavor, promising to revolutionize product development through enhanced data analytics, machine learning, and digital twins. Yet, the adoption of such technologies is uneven, primarily focusing on drug discovery with less emphasis on process development, which is critical for cost reduction and efficiency gains.
Challenges in Biopharmaceutical Development
The industry grapples with significant challenges that undermine the efficiency and economic viability of drug development. The high costs associated with R&D needed to develop new medicines are exacerbated by the extensive timelines required to bring a biopharmaceutical to market. These timelines are further stretched by the increasing complexity of biologics manufacturing and rising failure rates during early clinical phases. The capitalized costs to develop a new biopharmaceutical are staggering, often reaching billions of dollars, with a diminishing return on investment observed over recent years. This decline in ROI is notably due to growing development costs and an extended time to market, which restricts the competition-free window for new innovator drugs.
Moreover, the surge in biosimilar competition threatens market exclusivity, compounding financial pressures. Concurrent demands from governments and insurers for lower drug prices add another layer of complexity, heightening the need for cost-effective drug development strategies. The product life cycle, from discovery through to regulatory approval, is fraught with hurdles that many candidates fail to overcome. This high attrition rate necessitates stringent process management to ensure that development efforts are not wasted, making innovation within this sector more critical than ever.
Regulators Push Innovation through QbD and Digitalization
The pharmaceutical industry operates within a highly complex and impactful regulatory environment, crucial for guiding how companies make decisions, develop practices, and interact within the ecosystem. This framework, primarily shaped by major regulatory bodies like the FDA and the EMA, affects every facet of pharmaceutical operations from drug development to market release and post-market surveillance and extends beyond simple compliance, guiding the industry toward innovative practices. The FDA’s "Current Good Manufacturing Practices (cGMPs) for the 21st Century" initiative and similar efforts advocate for efficient manufacturing methods that maintain high quality without excessive regulatory oversight. Furthermore, initiatives promoting process analytical technology (PAT) and quality by design (QbD) represent a shift toward a more scientific and risk-managed approach to pharmaceutical development and manufacturing.5–8
QbD begins by defining a quality target product profile and translating these targets into critical quality attributes, which guide the development of a robust manufacturing process. This framework includes risk assessments, defining design spaces to allow for flexible manufacturing operations, and continuous process verification to enhance product quality proactively rather than relying solely on end-product testing.
Despite its benefits, the adoption of QbD has been gradual, in part because of the substantial initial investment required to implement these methodologies. Concerns about increased regulatory scrutiny and the potential complexities revealed by QbD-driven data further contribute to its cautious uptake.
The pharmaceutical industry needs to embrace advanced digital technologies within the QbD framework to enhance efficiency and adapt to the evolving demands of modern drug development. This evolution toward what is sometimes referred to as "Pharma 4.0TM" integrates cutting-edge technologies to streamline and optimize manufacturing processes, reflecting a modernization of the industry akin to transformations in other sectors. The successful implementation of QbD not only depends on regulatory encouragement but also on cultural shifts within companies to foster continuous improvement and innovation.
Digital Transformation in Biopharma and Its Challenges
In response to these enduring challenges, the biopharmaceutical sector has turned toward digital transformation as a strategic lever to enhance process efficiency and reduce developmental timelines. The integration of digital technologies such as artificial intelligence (AI), machine learning, and big data analytics represents a paradigm shift, offering potential solutions to longstanding inefficiencies. These technologies, including advanced online sensors, cloud-based data management, and AI-driven adaptive control systems, are poised to revolutionize pharmaceutical manufacturing by supporting more precise, real-time, and collaborative operations.
However, despite the promising advancements, the focus of digital initiatives, in particular AI, has largely been limited to drug discovery phases, with process development often overlooked. This oversight is critical as the potential benefits of digital technologies in process optimization are substantial yet remain largely unrealized in practical settings.
This slow adoption is partly due to the significant initial investments required, coupled with a cautious approach from companies fearing increased regulatory scrutiny. Additionally, there exists a gap in the comprehensive integration of these technologies, with many companies hesitant to fully apply digital methods in regulatory submissions, despite their potential to streamline and enhance manufacturing processes. As such, while digital transformation holds the key to future advancements in biopharmaceutical development, its full potential has yet to be harnessed, calling for a more concerted effort from all stakeholders involved.
Most initiatives in the industry, moreover, focus on drug discovery, with process development receiving much less attention despite the potential benefits that could be realized. Moreover, as AI and machine learning investments continue to accelerate drug discovery, generating a higher throughput of viable candidates, the demand on process development will intensify. This surge necessitates a proactive adaptation of process development strategies to avoid bottlenecks and ensure market readiness, capitalizing on the growing opportunities without risking delays or increased costs.
The Economics of Biopharmaceutical Process Development
Developing a robust and efficient biomanufacturing process, from cell culture and harvest through multiple downstream purification steps, necessitates managing hundreds to thousands of parameters. This begins with selecting the optimal expression system and extends to final formulation design. A thorough understanding of each unit operation is critical, encompassing phases from feasibility and exploration to optimization, scale-up, characterization, and validation. Frequently, knowledge generated across different process operations at varying scales by diverse teams suffers from limited sharing, leading to substantial knowledge loss, poor decision-making, and minimal automation and flexibility.
A drug development life cycle cost model developed by Suzanne S. Farid and colleagues9 provides a detailed breakdown of expenses specific to biopharmaceutical drug development, distinguishing it from traditional small molecule drug development. This model accounts for various stages of drug development, from preclinical phases through to clinical trials, and focuses on the allocation of resources and the success rates at each phase. In addition, the expenses are categorized in costs of clinical trials, process development, and material production for clinical trials. According to Farid's framework, the estimated expenses for process development necessary to achieve a successful product launch are substantial, totaling approximately $263 million, which represents 17% of the total development costs of $1.586 billion (out-of-pocket expenses were converted to capitalized costs).
Separately, the BioSolve Process economic modeling software was used to estimate the COGS for traditional manufacturing of biopharmaceuticals.10 For this analysis, a single-use, fed-batch process for monoclonal antibody production was modeled, including twelve distinct unit operations and a minimum annual production capacity of 500 kg. The annual COGS calculated through BioSolve Process was $86.8 million. Considering a manufacturing duration of 10 years before the patent expires and biosimilar competition starts drastically decreasing the profits, the total COGS amounts to $868 million. The significant cost difference between process development and the traditional manufacturing COGS, which is 3.3 times higher, highlights the financial importance of process development within the overall product life cycle management.
Expanding Horizons with Digital Process Development
Recent studies, such as the comprehensive review by Garcia-Munoz and colleagues,11 indicate that, while the pharmaceutical industry is increasingly adopting knowledge-driven and hybrid models across the biotherapeutic life cycle, data-driven models remain prevalent. Destro and colleagues nicely illustrated how mathematical modeling can support the implementation of QbD across all stages of development and manufacturing, namely design space description, process monitoring, and active process control.12
Digital twins are gaining traction in process development and manufacturing. These systems consist of a physical unit operation, a corresponding virtual model, and a bidirectional data-exchange infrastructure connecting the physical and virtual components, allowing real-time data flow and autonomous process adjustments through algorithms and historical data analysis. This integration frequently involves supervisory control and data acquisition (SCADA)systems to manage and optimize operational settings.
However, the direct application of AI and machine learning solutions in biopharmaceutical process development is constrained by data scarcity, making pure AI models less feasible. Hence, hybrid modeling — blending the adaptability of data-driven models and the rigor of knowledge-based approaches — offers a promising solution. This methodology enhances model accuracy, especially when data are limited, and supports the prediction of realistic outcomes beyond the initial data range. Moreover, hybrid models facilitate a deeper understanding of physical processes by correlating learned data patterns with concrete physical parameters. This approach not only enriches process knowledge but also aligns with QbD objectives, ensuring models are both interpretable and actionable.13–16
Unlocking Manufacturing Cost Reductions Through Digital Process Development
Comprehensive evaluation of the total business impact of digitalization in biopharmaceuticals is challenging owing to the nascent stage of digital innovations in the sector and the intangible or longer-term benefits they often bring. However, we can approximate the economic impact of digital process development by referencing published studies and documented instances of trends and catalysts for digital process development that combine tangible and intangible benefits.
For this analysis, we compared traditional and digital process development strategies using a full-factorial scenario analysis to assess potential COGS savings. The analysis treated the process development unit as a singular entity, employing two-dimensional mathematical optimization, including a conventional grid search technique and Bayesian optimization enriched with prior knowledge. This approach helped to roughly approximate the potential correlation between development efforts and enhancements in process development performance, translating these into measurable COGS reductions for the associated manufacturing processes.
Scenario analysis explored six factors — product titer, step yields, batch failure rates, labor, use of consumables, and costs associated with quality testing — for the benchmark process across 729 unique scenarios to gauge potential improvements. A manufacturing cost model quantified the possible COGS savings at scale.
The findings indicated that the most significant savings derived from maximizing product titer and improving yields. Lesser savings were associated with reductions in batch failure rates and consumables usage, while decreases in labor and quality testing costs had only minimal impacts, since a very conservative automation degree (+20%) and quality testing reductions (–20%) were assumed. Despite being approximations, these results align with expected patterns in traditional versus digital process development, revealing a considerable disparity in costs — from $86.8 million per year in a baseline scenario to $27.8 million in an optimized scenario, suggesting potential annual COGS savings of up to $59.9 million, or a 70% reduction.
Strategic Approaches to Enhancing Efficiency through Digital Process Development
Implementing digital process development eventually results in process development efficiency gains, independent of a pharmaceutical company’s digital maturity, organizational structure, and culture. Four primary strategies for leveraging digital process development efficiencies and their economic benefits were compared, each aligning with different management goals and organizational layers:
Resource optimization: This strategy involves reducing the scale of the process development department to cut costs. It's often considered by heads of development departments aiming to optimize financial performance within their sectors. Although this approach can be limited by the fixed nature of many expenses, over time, the acquisition of knowledge might enable reductions in these fixed costs.
Manufacturing performance maximization: This approach focuses on lowering the COGS by enhancing product titer and yields and reducing batch failure rates during manufacturing, through investing freed-up efficiency gains in process development. Typically championed by manufacturing heads, this strategy seeks to enhance financial outcomes across the product's life cycle.
Development funnel expansion: Instead of cutting process development resources, this strategy reallocates them to expand the development pipeline, potentially increasing the chances of successful drug launches. This broader approach requires a corporate-wide perspective, making it suitable for portfolio managers or higher-level executives.
Opportunistic hybrid strategy: Combining elements of the second and third strategies, this mixed approach invests freed-up efficiencies into manufacturing performance maximization to only an economical reasonable extent and allocates the remaining efficiencies in the development funnel expansion. This strategy adapts based on increasing experience and knowledge. It is the most comprehensive and holistic strategy, considering the entire organizational structure to maximize efficiency and financial returns.
The financial impact of these strategies can be substantial. Assuming a 50% efficiency gain, the four strategies could respectively yield $131.5 million, $560 million, $776 million, and $1.25 billion returns over a 10-year product lifespan, representing 10% of the average net income of the five largest pharmaceutical companies. For a company like Novartis with a portfolio of 10 blockbusters in 2020, that would translate to total savings of $12.5 billion with a full portfolio implementation. However, it's crucial to consider the initial costs of implementing digital process development solutions, estimated at $49.6 million.
When estimates of implementation expenses for the various strategies are considered, it becomes evident that the true value of digital transformation is not merely in choosing a strategy that fits but in committing to the highest level of digital integration possible across the organization. Strategies 2 and 4, which involve comprehensive digital adoption, achieve immediate break-even due to substantial COGS savings during manufacturing. In contrast, more conservative approaches like strategies 1 and 3 require significant efficiency gains of 19% and 3.5%, respectively, just to break even. This disparity underscores the critical importance of full-scale digital transformation — not only to achieve a favorable financial outcome but also to ensure that the organization remains competitive and capable of realizing high returns on investment. Pursuing partial measures or half-hearted digital strategies could result in suboptimal outcomes and missed opportunities.
Financial, Technical, and Organizational Challenges Must be Considered
The implementation of digital process development encounters challenges across financial, technical, and organizational domains. Interestingly, while digital transformation initiatives often enjoy robust funding, financial challenges are not generally the greatest challenge, at least for widely recognized applications. The primary difficulty in digital process development lies in pinpointing effective metrics to accurately gauge and monitor its business impact.
Technical challenges: Simpler digital tools are more readily adopted due to their ease of use. In contrast, complex solutions requiring specialized skills face slower adoption rates. Managing a diverse array of software solutions daily proves cumbersome for operators and research scientists. Furthermore, a lack of standardization complicates the integration of various digital solutions, leading to substantial manual burdens. Additionally, insufficient awareness and utilization of model-based design strategies often result in data that lacks the depth and quality necessary for advanced modeling.
Organizational challenges: These are frequently the most significant barriers to the implementation of digital process development. Adopting digitalization and QbD methodologies involves more than just integrating new technologies or allocating financial resources; it requires a profound transformation of both processes and mindsets. Organizational inertia can stymie this shift, making it one of the toughest hurdles to overcome.
Debates about whether to develop digital solutions in-house or to adopt external tools often delay digitalization efforts. Poorly designed in-house solutions and the lack of resources for their maintenance can impede progress. Additionally, the absence of a cohesive digitalization strategy, with a tendency toward pursuing isolated initiatives, diminishes the overall benefits of digital transformation. Resistance to change represents a significant obstacle, one that can only be surmounted through strong leadership and a culture that actively supports digital innovation.
To effectively navigate digital transformation challenges, it is essential to form a dedicated team of experts, including data scientists, process modelers, data managers, IT infrastructure specialists, software engineers, and knowledge managers. However, beyond assembling this team, it's crucial to empower process engineers and operators to actively utilize these digital solutions in their daily work. This empowerment ensures that the tools developed are practical and user-friendly and directly enhance operational efficiency. The expert team must work closely with on-the-ground staff to tailor digital initiatives and provide the necessary training, fostering a culture of collaboration and continuous improvement across all levels of the organization.
Maximizing Financial Returns using Digital Process Development
As is the case with modeling of any business activities, certain assumptions were made in calculating the potential economic benefits of implementing digital process development solutions. The obtained findings, therefore, should be viewed as indicative of overarching trends rather than absolute values. Even so, they clearly show that digital process development can substantially impact financial outcomes — as much as a 70% reduction in COGS, with most benefit gained when freed-up process development resources are allocated toward enhancing manufacturing performance to a reasonable extent and simultaneously expanding the development pipeline.
Significantly, the pharmaceutical industry reportedly faces annual losses of approximately $50 billion due to suboptimal manufacturing practices.17 Digital process development can play a pivotal role in curtailing these extensive, albeit often overlooked, losses. It can also help pharmaceutical companies avoid costly sanctions from regulatory authorities due to manufacturing deficiencies, with penalties ranging from minor fines (e.g., –410,000) to costs in the millions, depending on the issue, and potentially exceeding $1 billion in cases requiring a consent decree.18 Employing digital innovations and QbD methodologies can mitigate these challenges, particularly when integrated with cutting-edge technologies.
While this analysis was centered on a platform monoclonal antibody (mAb) process, it suggests that the financial impacts could be even more significant for complex therapeutics, such as cell therapies. This is particularly true for autologous, patient-specific products that require adaptations of process development for individual patient profiles. Additionally, the growing contributions of advanced algorithms like hybrid modeling, transfer learning, and active learning in knowledge capture and management are poised to broaden their business impact across various therapeutic modalities.
In the long term, leveraging data insights, knowledge management, and re-utilization of knowledge to build a vast knowledge base enabling digital process development will prove advantageous. Over time, this approach is expected to provide a sustainable competitive edge for those manufacturers willing to begin their digital process development journey today. Companies that choose not to adopt these practices, however, may find themselves at a disadvantage, potentially missing out on critical momentum in the industry.
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