The bioprocess industry originated with the development of fermentation-derived small molecules and later expanded to include biologics and other advanced therapeutics. Early successes in antibiotics and immunosuppressants demonstrated the potential of microbial bioprocesses to deliver life-saving drugs. Over time, technological advancements in strain engineering, upstream process development, and downstream purification reshaped the industry, enabling the efficient manufacture of both small molecules and complex biologics. Today, bioprocess engineering integrates synthetic biology, precision fermentation, automation, process analytical technology (PAT), and digital twins to create smart, sustainable, and scalable platforms for pharmaceutical manufacturing.

Strain Engineering: From Random Mutagenesis to Genetic Precision

Early strain improvement relied on screening of natural isolates and random mutagenesis, followed by laborious screening of microbial variants. These approaches, though foundational, were inefficient and unpredictable. Genetic engineering has transformed strain development by enabling targeted DNA modifications. Modern CRISPR-based engineering and automated strain optimization platforms facilitate precise metabolic pathway rewiring, accelerating the development of high-performance microbial cell factories (Iwai et al., 2022). More recently, machine learning-assisted strain design has complemented modern strain engineering approaches by enabling the predictive identification of high-yielding microbial variants and guiding metabolic pathway optimization (Strüssmann et al., 2025).

For small molecules such as tacrolimus and sirolimus, pathway optimization has increased yields by minimizing by-product formation and enhancing precursor supply. Similarly, daptomycin production has benefited from rational engineering of Streptomyces strains, improving productivity and reducing costs (Li et al., 2024). This transition from empirical screening to rational design represents a paradigm shift in bioprocess engineering.

Upstream Process Development: From Strain to Bioreactors

Once a strain is optimized, it undergoes upstream process development to establish cultivation conditions that can be reliably translated from laboratory to manufacturing scale. Scale-up typically begins in shake flasks. Despite the emergence of microbioreactors and high-throughput platforms, shake flasks remain the preferred tool for early process optimization due to their simplicity, scalability, and lower cost. As processes move across scales, maintaining consistent growth conditions, oxygen transfer, and nutrient availability becomes critical. To address these challenges, modern upstream bioprocesses increasingly rely on advanced monitoring technologies, PAT, and model-based control strategies to improve process understanding, robustness, and operational efficiency (Kornecki & Strube, 2019). Continuous bioprocessing, intensified feeding strategies, and, in some applications, perfusion-based cultivation have further enhanced productivity. These innovations are particularly impactful for small-molecule fermentation, where oxygen transfer and nutrient balance are critical for maximizing yields. For example, optimized feeding strategies (Wang et al., 2017) and strategies to reduce product inhibition (Zhang et al., 2025) have enhanced tacrolimus production, while rational metabolic engineering has increased daptomycin titers (Li et al., 2024).

Automation reduces variability, shortens development timelines, and supports reproducibility across scales, making upstream operations a cornerstone of modern bioprocess engineering. Machine learning models are increasingly being used to forecast biomass growth, substrate consumption, oxygen demand, and product titer, enabling dynamic optimization of feeding strategies and reducing process variability (Strüssmann et al., 2025). Extending this concept, Gasset et al., 2024 demonstrated Continued Process Verification (CPV) 4.0 in upstream bioprocessing using Pichia pastoris under hypoxic fed-batch conditions by integrating IoT-enabled sensors, a digital twin, edge/cloud computing, and AI models to enable real-time monitoring and adaptive process control. The AI-aided Adaptive-Proportional Control (AI-APC) strategy improved reproducibility and reduced the Mean Relative Error (MRE) to below 4%, compared with 10% for manual heuristic control and 5% for Boolean logic control. This study demonstrated how AI-aided adaptive control meaningfully outperforms both manual and rule-based control strategies in upstream bioprocessing, rather than merely automating existing practice.

Downstream Processing: The advent of continuous processing and green chemistry

Traditional downstream processing methods such as solvent extraction and crystallization are being optimized and supplemented to maximize purity of the final product. Modern downstream operations such as membrane-based separations (Makoś-Chełstowska et al., 2021; Díaz-Montes and Castro-Muñoz, 2021) and continuous purification platforms, including multi-column chromatography systems, are increasingly being adopted to intensify downstream processing and enable integrated continuous biomanufacturing (Kruse et al., 2026). In downstream processing, these technologies support advanced automation through predictive control of separation, purification, and recovery operations, improving process robustness, yield, and consistency while enabling Continued Process Verification (CPV) and facilitating scale-up to commercial manufacturing (Chen et al., 2020). Furthermore, innovations such as simulated moving bed (SMB) chromatography have enabled the efficient continuous separation of closely related compounds while reducing solvent consumption and improving product purity, as described in Korean patent KR101344012B1. Together, these technologies enhance recovery, purity, and efficiency, aligning pharmaceutical manufacturing with sustainability goals.

End-to-End Manufacturing of Small Molecules in the 21st Century

Modern pharmaceutical manufacturing is increasingly adopting Industry 4.0 principles to create connected, intelligent, and data-driven production environments. Through the integration of industrial IoT sensors, automated control systems, cloud computing, advanced analytics, and digital twins, data can be captured and analyzed across upstream processing, downstream purification, quality assurance, and manufacturing operations. These interconnected cyber-physical systems enable real-time process monitoring, predictive maintenance, adaptive process control, and data-driven decision-making throughout the production lifecycle. Digital twins further support virtual process simulation, scenario testing, and Continued Process Verification (CPV), allowing manufacturers to proactively optimize performance, reduce operational risk, improve product consistency, and enhance manufacturing efficiency. Collectively, these Industry 4.0 technologies are driving the transition from traditional batch operations toward smarter, more agile, and digitally integrated biomanufacturing platforms (Narayanan et al., 2022; Richter et al., 2022).

Conclusion

Bioprocess engineering has evolved from rudimentary fermentation processes used for the production of small molecules to a highly sophisticated discipline encompassing strain engineering, advanced upstream operations, automated process monitoring, and intensified downstream purification. The convergence of synthetic biology, automation, PAT, digital twins, and Industry 4.0 technologies is driving the transition toward smart, sustainable, and flexible biomanufacturing platforms (Narayanan et al., 2022; Richter et al., 2022). Together, these innovations are improving process efficiency, robustness, and product quality across the bioprocess lifecycle. As the demand for fermentation-derived therapeutics continues to grow, the continued adoption of advanced bioprocess engineering technologies will be critical for delivering more sustainable, economically viable, and accessible pharmaceutical manufacturing solutions.