A new study published in Case Studies in Chemical and Environmental Engineering tackles both problems at once. A research team from Khon Kaen University in Thailand, led by Siriporn Lunprom and Apilak Salakkam, combined a carefully optimized biomass-to-sugar pipeline with a pair of upgraded mathematical models that, for the first time in the Luedeking–Piret framework, explicitly account for the two phenomena that have quietly undermined previous PHB fermentation studies: cell death and the microbial consumption of the very polymer the cells have just made. The work demonstrates that sugarcane bagasse can yield record PHB titers when the process is run in fed-batch mode, and that modified kinetic models can pinpoint exactly when to stop a fermentation to avoid losing product.

The raw material preparation followed a two-stage strategy. First, bagasse was pretreated with alkaline hydrogen peroxide at pH 11.6 and 60 °C for six hours, a treatment that swells and delignifies the fibrous matrix. Composition analysis showed the pretreatment transformed the material from 36.5 percent cellulose, 23.1 percent hemicellulose, and 26.9 percent lignin into a fiber fraction containing 59.1 percent cellulose with lignin reduced to just 8.3 percent. Next, enzymatic saccharification using the commercial cellulase Cellic CTec3 together with a xylanase preparation released a hydrolysate containing 103 grams per liter of reducing sugar, dominated by glucose with a smaller xylose fraction. A fivefold concentrated version of the hydrolysate, containing nearly 499 grams per liter of sugar, served as the feeding solution for later fed-batch experiments.

The producing organism, Paracoccus sp. KKU01, was originally isolated from soil at a cassava starch plant and is known to convert sugars efficiently into PHB. In preliminary shake-flask fermentations on the bagasse hydrolysate, the bacterium grew steadily, consumed glucose over 108 hours, and accumulated up to 5.9 grams per liter of PHB. Interestingly, the xylose in the medium decreased only slowly, and genomic reasoning based on the KEGG database suggested that this strain, like other Paracoccus species, lacks the key enzymes for xylose metabolism, namely xylose isomerase and xylulokinase. The team therefore treated glucose as the true carbon source for growth and polymer synthesis, and confirmed that xylose played no role in either process.

When the process was scaled to a controlled 2-liter bioreactor, performance improved dramatically. The residual cell dry mass climbed rapidly to a peak of 32.9 grams per liter at 36 hours, and PHB production, first detected at 18 hours, reached 8.3 grams per liter by 42 hours — a 1.4-fold higher titer than in flasks, with productivity improving 3.3-fold thanks to precise control of pH, temperature, and dissolved oxygen. But then came the observation that motivated the entire modeling effort: after glucose was exhausted, both biomass and PHB concentrations fell. The polymer, stored intracellularly as a carbon reserve, was being mobilized by the starving cells as an energy source, a natural turnover response that classical fermentation models simply do not describe.

That limitation is rooted in the history of the models themselves. The logistic model, originally devised for human population growth, describes biomass rising smoothly toward a carrying capacity but can never predict a decline. The Luedeking–Piret model, developed in 1959 for lactic acid — a product its producers do not consume — treats product formation as the sum of a growth-associated and a non-growth-associated term and therefore predicts endless accumulation. To fix this, the researchers added a first-order cell death term to the logistic model and a first-order consumption term to the Luedeking–Piret equation, then reformulated both as piecewise change-point models. Growth and PHB accumulation follow classical kinetics until critical times t-crit and t-crit2, after which the models switch to exponential decay governed by a specific death rate and a specific PHB consumption rate. Crucially, the added complexity stays minimal: the AICc criterion confirmed that the extra parameters improved fit rather than chasing noise.

The numbers validated the approach. In the batch bioreactor, the modified logistic model raised the coefficient of determination from 0.9734 to 0.9927 and correctly captured the biomass decline beginning at a predicted critical time of 34.38 hours. The modified Luedeking–Piret model performed even more strikingly, lifting the fit for PHB from a poor 0.6960 to 0.9581 and predicting a PHB peak of 8.34 grams per liter at 41.75 hours with a consumption rate of 0.034 per hour. The fitted parameters also revealed the physiology of the process: the growth-associated coefficient approached zero, meaning essentially all PHB was synthesized during the stationary phase, consistent with nitrogen-limited conditions after the cells stopped dividing.

Fed-batch operation pushed the process further. By feeding concentrated hydrolysate twice, at 36 and 51 hours, whenever glucose dropped below about 15 grams per liter, the team raised the PHB titer to 14.1 grams per liter at 66 hours — roughly 70 percent above the batch result and the highest value yet reported for PHB production from sugarcane bagasse — while yield and productivity held steady at 0.14 grams per gram glucose and 0.21 grams per liter per hour. Again, the modified models proved their worth, tracking the biomass and polymer declines that began after 66 hours, likely driven by the depletion of nutrients other than carbon, since only glucose was being fed. The classical models, by contrast, predicted meaningless continuous accumulation and would have misled any attempt to time the harvest.

To show the framework was not tuned to one system, the team fitted the modified models to published datasets from five unrelated fermentations, including recombinant Ralstonia eutropha growing on glycerol, Bacillus megaterium on bagasse, Cupriavidus necator on corn hydrolysate, and two oleaginous yeasts producing microbial lipids from corn stover, one of them at 1000-liter pilot scale. Across these external systems the models achieved coefficients of determination as high as 0.997 and accuracy factors close to the ideal value of one, correctly identifying decay onsets where they occurred and reporting negligible decay where product levels never fell. This versatility matters because intracellular products such as PHB and microbial lipids share the same fundamental vulnerability: once carbon runs out, the producing cells eat their own reserves.

The practical implications reach beyond bioplastics. Because the modified models estimate the peak product concentration and the exact time it occurs, they offer a parsimonious engineering tool for scheduling harvests, minimizing losses, and designing feeding strategies, all while requiring nothing more than routine biomass and product measurements. The Thai team, supported by Khon Kaen University and the National Science, Research and Innovation Fund, emphasizes that further optimization of polymer content and productivity is still needed before bagasse-derived PHB becomes commercially viable. But the combination of a record titer from an abundant agricultural waste and a modeling framework that finally tells operators when the clock is ticking on their product brings the economics of bacterial plastic one significant step closer to reality.

Subject of Research: Kinetic modeling of PHB bioplastic production from sugarcane bagasse by Paracoccus sp. KKU01

Article Title: Poly(3-hydroxybutyrate) production from sugarcane bagasse by Paracoccus sp.: Kinetic modeling using modified logistic and Luedeking–Piret models incorporating cell death and PHB consumption

Article References: Lunprom, S., Jureemas, N., Komat, P., Moungprayoon, A., Khanpanuek, S., & Salakkam, A. (2026). Poly(3-hydroxybutyrate) production from sugarcane bagasse by Paracoccus sp.: Kinetic modeling using modified logistic and Luedeking–Piret models incorporating cell death and PHB consumption. Case Studies in Chemical and Environmental Engineering, 14, Article 101474. https://doi.org/10.1016/j.cscee.2026.101474

Image Credits: AI Generated

DOI: 10.1016/j.cscee.2026.101474

Keywords: PHB, bioplastic, sugarcane bagasse, Paracoccus sp. KKU01, kinetic modeling, Luedeking-Piret model, logistic model, fed-batch fermentation, PHB mobilization, biorefinery, cell death, lignocellulosic hydrolysate

At the heart of this research lies the Luedeking-Piret model—a well-established kinetic framework traditionally utilized to describe metabolite production rates relative to microbial growth rates. Historically, this model captured both growth-associated and non-growth-associated product formation rates in batch and fed-batch cultures. What distinguishes the current study is the application of this classical regression within a novel multi-step-ahead forecasting context tailored explicitly for the idiosyncratic nature of monoclonal antibody production, a domain marked by nonlinear dynamics and multi-phase bioprocess stages.

Monoclonal antibodies are produced through the careful cultivation of specific clones of genetically engineered cells capable of high-yield protein expression. The selection and maintenance of these clones highly influence the final product’s quality and quantity. Yet, clone behavior variability over extended production runs often leads to significant fluctuations in output. Therefore, the ability to predict performance several steps ahead in the production timeline is invaluable, enabling proactive adjustments that optimize both bioprocess stability and resource allocation.

By integrating Luedeking-Piret regression within a multi-step forecasting framework, the authors have addressed a critical gap in predictive bioprocessing. Unlike traditional one-step forecasts, which only provide information for the immediate next process state, multi-step predictions allow operators to anticipate and mediate deviations over an extended horizon. This advance becomes particularly relevant in the context of clone selection where early identification of underperforming clones can save vast amounts of time and production costs.

The research team developed a sophisticated computational pipeline that trains the regression model on historical bioprocess data encompassing multiple cultivation phases—including inoculum expansion, bioreactor cultivation, and downstream purification. By capturing the kinetic interplay between cell growth, metabolite consumption, and product formation, their model robustly captures temporal dependencies often missed by purely empirical or static models. Crucially, this approach accommodates shifts in clone behavior induced by subtle environmental changes or intrinsic genetic drift.

Technically, the model leverages time-series regression methodologies augmented with dynamic parameter estimation. This design facilitates adaptive calibration, enabling the system to remain accurate across fluctuating bioprocess conditions. The multi-step-ahead forecasting is implemented using recursive prediction schemes, where the model’s outputs at each time step feed back as inputs for subsequent predictions. This iterative mechanism mirrors real biomanufacturing dynamics, allowing for nuanced anticipation of product titer, cell viability, and metabolic indicators multiple steps into the future.

Validation results reported in the study are compelling. The Luedeking-Piret based multi-step model demonstrated superior predictive accuracy compared to benchmark models such as classical state-space models and machine learning black-box approaches. Moreover, its interpretability offers critical biochemical insight, aiding process engineers in discerning underlying causes behind forecasted trends—an advantage rare in purely data-driven methods. These attributes are essential for regulatory compliance and quality-by-design initiatives that increasingly shape pharmaceutical manufacturing.

Another profound implication of this research is its integration potential within digital biomanufacturing ecosystems. As pharmaceutical production embraces Industry 4.0 paradigms, encompassing automation, artificial intelligence, and real-time process monitoring, having robust forecasting models embedded within control frameworks becomes indispensable. The Luedeking-Piret regression model’s balance of mechanistic understanding and predictive power perfectly aligns with this vision, enabling smarter, autonomous production environments capable of continuous optimization.

From a broader perspective, this advancement also impacts bioprocess sustainability. Enhanced forecasting and early clone performance assessment reduce wasted raw materials, energy consumption, and labor—mitigating the environmental footprint intrinsic to biologics manufacturing. Such improvements resonate with ongoing industry commitments to greener pharmaceutical production pathways, fostering responsible innovation aligned with public health and environmental stewardship.

The research team emphasizes that while their methodology centers on monoclonal antibodies, the underlying principles are extensible to other complex bioproducts reliant on cell-based production systems. Whether producing viral vectors, recombinant proteins, or enzyme therapeutics, advanced kinetic regression models tailored for multi-step forecasting could become standard tools, ushering new levels of precision and agility in biomanufacturing.

Furthermore, this study opens new avenues for interdisciplinary collaboration. Bridging computational modeling with experimental bioprocessing, process systems engineering, and data science propels the field toward more integrated solutions. Future work exploring hybrid models that combine Luedeking-Piret kinetics with mechanistic metabolic network reconstructions or deep learning architectures could unlock even higher accuracy and adaptability.

The practicalities of implementing this model in industrial settings are also discussed. Adoption challenges such as data availability, sensor integration, and model maintenance are acknowledged but deemed manageable within current technological trends. The authors provide guidelines for model training, validation procedures, and impact assessment metrics, facilitating technology transfer and adoption by biopharmaceutical manufacturers eager to enhance process robustness and product consistency.

In summarizing the study’s contribution, it is evident that Wang, Verma, Chiu, and their colleagues have made a significant leap in predictive bioprocess modeling. Their innovative application of Luedeking-Piret regression for multi-step-ahead forecasting and clone selection exemplifies how classical biochemical engineering principles, when artfully integrated with modern computational tools, can unlock new potentials in biomanufacturing. This innovation paves the way for more efficient, scalable, and sustainable production of monoclonal antibodies, therapies that continue to transform modern medicine.

Looking ahead, as biologics diversify and personalized medicine demands increasingly flexible manufacturing platforms, such predictive methodologies will be indispensable. Real-time decision support systems empowered by advanced kinetic regression models may soon become the norm rather than the exception, fundamentally reshaping how therapeutics are produced and delivered worldwide.

The impact of this research extends beyond technical circles. By addressing core challenges in monoclonal antibody production—one of the pharmaceutical sector’s most critical and rapidly expanding areas—this study resonates with a broad audience invested in accelerating healthcare innovations. From patients awaiting life-saving treatments to manufacturers striving for operational excellence, the implications are profound and far-reaching.

Ultimately, the integration of Luedeking-Piret regression into multi-step forecasting and clone selection signals a new epoch for biomanufacturing science and engineering. As digital transformation continues to permeate the sector, harnessing such advanced models will be crucial in meeting global demand for biologics with unparalleled quality, consistency, and efficiency. This research stands as a testament to the power of interdisciplinary innovation in advancing public health through engineering excellence.

Subject of Research: Monoclonal antibodies biomanufacturing optimization through kinetic regression modeling and multi-step-ahead forecasting.

Article Title: Luedeking-Piret regression for multi-step-ahead forecasting and clone selection in monoclonal antibodies biomanufacturing.

Article References:
Wang, P., Verma, D., Chiu, Y. et al. Luedeking-Piret regression for multi-step-ahead forecasting and clone selection in monoclonal antibodies biomanufacturing. Commun Eng (2025). https://doi.org/10.1038/s44172-025-00547-7

Image Credits: AI Generated