Design of experiments and design space approaches in the pharmaceutical bioprocess optimization

Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review

This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field.

Machine learning: an advancement in biochemical engineering

One of the most remarkable techniques recently introduced into the field of bioprocess engineering is machine learning. Bioprocess engineering has drawn much attention due to its vast application in different domains like biopharmaceuticals, fossil fuel alternatives, environmental remediation, and food and beverage industry, etc. However, due to their unpredictable mechanisms, they are very often challenging to optimize. Furthermore, biological systems are extremely complicated; hence, machine learning algorithms could potentially be utilized to improve and build new biotechnological processes. Gaining insight into the fundamental mathematical understanding of commonly used machine learning algorithms, including Support Vector Machine, Principal Component Analysis, Partial Least Squares and Reinforcement Learning, the present study aims to discuss various case studies related to the application of machine learning in bioprocess engineering. Recent advancements as well as challenges posed in this area along with their potential solutions are also presented.

A Quality by Design Approach for Optimizing Solid Lipid Nanoparticles of Bedaquiline for Improved Product Performance

Bedaquiline (BQ) solid lipid nanoparticles (SLNs), which have previously been formulated for parenteral administration, have a risk of patient non-compliance in treating tuberculosis. This research presents a strategy to develop BQ SLNs for oral delivery to improve patient adherence, The upper and lower levels for the formulation excipients were generated from screening experiments. Using 4 input factors (BQ, lecithin, Tween 80, and PEG), a full factorial design from 3 × 2x2 × 2 experiments was randomly arranged to investigate 3 response variables: Particle size distribution (PSD), polydispersity index (PdI), and zeta potential (ZP). High shear homogenization was used to mix the solvent and aqueous phases, with 15% sucrose as a cryoprotectant. The response variables were assessed using a zeta sizer while TEM micrographs confirmed the PSD data. Solid-state assessments were conducted using powdered X-ray diffraction and scanning electron microscopy (SEM) imaging. A comparative invitro assessment was used to determine drug release from an equivalent dose of BQ free base powder and BQ-SLN, both packed in hard gelatin capsules. The sonicated formulations obtained significant effects for PSD, PdI, and ZP. The p-values (0.0001 for PdI, 0.0091 for PSD) for BQ as an independent variable in the sonicated formulation were notably higher than those in the unsonicated formulation (0.1336 for PdI, 0.0117 for PSD). The SEM images were between 100 - 400 nm and delineated nanocrystals of BQ embedded in the lipid matrix. The SLN formulation provides higher drug levels over the drug's free base; a similarity factor (f2 = 18.3) was estimated from the dissolution profiles.

Investigation of the Electrokinetic Potential of Granules and Optimization of the Pelletization Method Using the Quality by Design Approach

The preparation of pellets using a high-shear granulator in a rapid single-step is considered a good economic alternative to the extrusion spheronization process. As process parameters and material attributes greatly affect pellet qualities, successful process optimization plays a vital role in producing pellet dosage forms with the required critical quality attributes. This study was aimed at the development and optimization of the pelletization technique with the Pro-CepT granulator. According to the quality by design (QbD) and screening design results, chopper speed, the volume of the granulating liquid, binder amount, and impeller speed were selected as the highest risk variables for a two-level full factorial design and central composite design, which were applied to the formula of microcrystalline cellulose, mannitol, and with a binding aqueous polyvinylpyrrolidone solution. The design space was estimated based on physical response results, including the total yield of the required size, hardness, and aspect ratio. The optimized point was tested with two different types of active ingredients. Amlodipine and hydrochlorothiazide were selected as model drugs and were loaded into an optimized formulation. The kinetics of the release of the active agent was examined and found that the results show a correlation with the electrokinetic potential because amlodipine besylate can be adsorbed on the surface of the MCC, while hydrochlorothiazide less so; therefore, in this case, the release of the active agent increases. The research results revealed no significant differences between plain and model drug pellets, except for hydrochlorothiazide yield percent, in addition to acceptable content uniformity and dissolution enhancement.

Bayesian cell therapy process optimization

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Optimizing Nanosuspension Drug Release and Wound Healing Using a Design of Experiments Approach: Improving the Drug Delivery Potential of NDH-4338 for Treating Chemical Burns

NDH-4338 is a highly lipophilic prodrug comprising indomethacin and an acetylcholinesterase inhibitor. A design of experiments approach was used to synthesize, characterize, and evaluate the wound healing efficacy of optimized NDH-4338 nanosuspensions against nitrogen mustard-induced skin injury. Nanosuspensions were prepared by sonoprecipitation in the presence of a Vitamin E TPGS aqueous stabilizer solution. Critical processing parameters and material attributes were optimized to reduce particle size and determine the effect on dissolution rate and burn healing efficacy. The antisolvent/solvent ratio (A/S), dose concentration (DC), and drug/stabilizer ratio (D/S) were the critical sonoprecipitation factors that control particle size. These factors were subjected to a Box-Behnken design and response surface analysis, and model quality was assessed. Maximize desirability and simulation experiment optimization approaches were used to determine nanosuspension parameters with the smallest size and the lowest defect rate within the 10-50 nm specification limits. Optimized and unoptimized nanosuspensions were prepared and characterized. An established depilatory double-disc mouse model was used to evaluate the healing of nitrogen mustard-induced dermal injuries. Optimized nanosuspensions (A/S = 6.2, DC = 2% w/v, D/S = 2.8) achieved a particle size of 31.46 nm with a narrow size range (PDI = 0.110) and a reduced defect rate (42.2 to 6.1%). The optimized nanosuspensions were stable and re-dispersible, and they showed a ~45% increase in cumulative drug release and significant edema reduction in mice. Optimized NDH-4338 nanosuspensions were smaller with more uniform sizes that led to improved physical stability, faster dissolution, and enhanced burn healing efficacy compared to unoptimized nanosuspensions.

COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling

Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.

Using design of experiments to guide genetic optimization of engineered metabolic pathways

Design of experiments (DoE) is a term used to describe the application of statistical approaches to interrogate the impact of many variables on the performance of a multivariate system. It is commonly used for process optimization in fields such as chemical engineering and material science. Recent advances in the ability to quantitatively control the expression of genes in biological systems open up the possibility to apply DoE for genetic optimization. In this review targeted to genetic and metabolic engineers, we introduce several approaches in DoE at a high level and describe instances wherein these were applied to interrogate or optimize engineered genetic systems. We discuss the challenges of applying DoE and propose strategies to mitigate these challenges.

ONE-SENTENCE SUMMARY

This is a review of literature related to applying Design of Experiments for genetic optimization.

Optimization and Validation of a Liquid Formulation for a New Recombinant Veterinary Product using QbD Approach

Protein formulation and drug characterization are one of the most difficult and time-consuming tasks because of the complexity of biotherapeutic proteins. Hence, maintaining a protein drug in its active state typically requires preventing changes in its physical and chemical properties. Quality by Design (QbD) is a systematic approach emphasizing product and process understanding. Design of Experiments (DoE) is one of the most important QbD tools, allowing the possibility to modify the formulation attributes within a defined design space. Here, we report the validation of a RP-HPLC assay for recombinant equine chorionic gonadotropin (reCG) that demonstrated a high correlation with the in vivo potency biological assay. QbD concepts were then applied to obtain an optimized liquid formulation of reCG with a predefined quality product profile. The developed strategy demonstrates the importance of applying multivariable strategies as DoE to simplify formulation stages, improving the quality of the obtained results. Moreover, it is important to highlight that this is the first time that a liquid formulation is reported for an eCG molecule, since, up to now, the only eCG products available in the market for veterinary use consisted in partially purified preparations of pregnant mare serum gonadotropin (PMSG) presented as a lyophilized product.

Computational Amendment of Parenteral In Situ Forming Particulates' Characteristics: Design of Experiment and PBPK Physiological Modeling

Lipid and/or polymer-based drug conjugates can potentially minimize side effects by increasing drug accumulation at target sites and thus augment patient compliance. Formulation factors can present a potent influence on the characteristics of the obtained systems. The selection of an appropriate solvent with satisfactory rheological properties, miscibility, and biocompatibility is essential to optimize drug release. This work presents a computational study of the effect of the basic formulation factors on the characteristics of the obtained in situ-forming particulates (IFPs) encapsulating a model drug using a 21.31 full factorial experimental design. The emulsion method was employed for the preparation of lipid and/or polymer-based IFPs. The IFP release profiles and parameters were computed. Additionally, a desirability study was carried out to choose the optimum formulation for further morphological examination, rheological study, and PBPK physiological modeling. Results revealed that the type of particulate forming agent (lipid/polymer) and the incorporation of structure additives like Brij 52 and Eudragit RL can effectively augment the release profile as well as the burst of the drug. The optimized formulation exhibited a pseudoplastic rheological behavior and yielded uniformly spherical-shaped dense particulates with a PS of 573.92 ± 23.5 nm upon injection. Physiological modeling simulation revealed the pioneer pharmacokinetic properties of the optimized formulation compared to the observed data. These results assure the importance of controlling the formulation factors during drug development, the potentiality of the optimized IFPs for the intramuscular delivery of piroxicam, and the reliability of PBPK physiological modeling in predicting the biological performance of new formulations with effective cost management.

A Customized Bayesian Algorithm to Optimize Enzyme-Catalyzed Reactions

Design of experiments (DoE) plays an important role in optimizing the catalytic performance of chemical reactions. The most commonly used DoE relies on the response surface methodology (RSM) to model the variable space of experimental conditions with the fewest number of experiments. However, the RSM leads to an exponential increase in the number of required experiments as the number of variables increases. Herein we describe a Bayesian optimization algorithm (BOA) to optimize the continuous parameters (e.g., temperature, reaction time, reactant and enzyme concentrations, etc.) of enzyme-catalyzed reactions with the aim of maximizing performance. Compared to existing Bayesian optimization methods, we propose an improved algorithm that leads to better results under limited resources and time for experiments. To validate the versatility of the BOA, we benchmarked its performance with biocatalytic C-C bond formation and amination for the optimization of the turnover number. Gratifyingly, up to 80% improvement compared to RSM and up to 360% improvement vs previous Bayesian optimization algorithms were obtained. Importantly, this strategy enabled simultaneous optimization of both the enzyme's activity and selectivity for cross-benzoin condensation.

Greedy design space construction based on regression and latent space extraction for pharmaceutical development

Implementation of the design space (DS) is a scientific concept for ensuring quality to be submitted as a part of the regulatory filing of a drug product for approval to market. An empirical approach is constructing the DS based on the regression model whose inputs are process parameters and material attributes over the different unit operations, i.e., a high-dimensional statistical model. While the high-dimensional model assures quality and process flexibility through a comprehensive process understanding, it has difficulty visualizing the feasible range of input parameters, i.e., DS. Therefore, this study proposes a greedy approach to constructing the extensive and flexible low-dimensional DS based on the high-dimensional statistical model and the observed internal representations that satisfies both comprehensive process understanding and the DS visualization capability. Introducing the observed correlation structure enabled the dimensionality reduction of the DS. The non-critical controllable parameters were fixed to the target values in visualizing the low-dimensional DS as a function of critical parameters. The expected variation of non-critical non-controllable parameters was considered the source of variation in prediction. The case study demonstrated the proposed approach's usefulness for developing the pharmaceutical manufacturing process.

The General Composition of Polyhydroxyalkanoates and Factors that Influence their Production and Biosynthesis

Polyhydroxyalkanoates (PHAs) have been a current research topic for many years. PHAs are biopolymers produced by bacteria under unfavorable growth conditions. They are biomaterials that exhibit a variety of properties, including biocompatibility, biodegradability, and high mechanical strength, making them suitable for future applications. This review aimed to provide general information on PHAs, such as their structure, classification, and parameters that affect the production process. In addition, the most commonly used bacterial strains that produce PHAs are highlighted, and details are provided on the type of carbon source used and how to optimize the parameters for bioprocesses. PHAs present a challenge to researchers because a variety of parameters affect biosynthesis, including the variety of carbon sources, bacterial strains, and culture media. Nevertheless, PHAs represent an opportunity to replace plastics, because they can be produced quickly and at a relatively low cost. With growing environmental concerns and declining oil reserves, polyhydroxyalkanoates are a potential replacement for nonbiodegradable polymers. Therefore, the study of PHA production remains a hot topic, as many substrates can be used as carbon sources. Both researchers and industry are interested in facilitating the production, commercialization, and application of PHAs as potential replacements for nonbiodegradable polymers. The fact that they are biocompatible, environmentally biodegradable, and adaptable makes PHAs one of the most important materials available in the market. They are preferred in various industries, such as agriculture (for bioremediation of oil-polluted sites, minimizing the toxicity of pollutants, and environmental impact) or medicine (as medical devices). The various bioprocess technologies mentioned earlier will be further investigated, such as the carbon source (to obtain a biopolymer with the lowest possible cost, such as glucose, various fatty acids, and especially renewable sources), pretreatment of the substrate (to increase the availability of the carbon source), and supplementation of the growth environment with different substances and minerals). Consequently, the study of PHA production remains a current topic because many substrates can be used as carbon sources. Obtaining PHA from renewable substrates (waste oil, coffee grounds, plant husks, etc.) contributes significantly to reducing PHA costs. Therefore, in this review, pure bacterial cultures (Bacillus megaterium, Ralstonia eutropha, Cupriavidus necator, and Pseudomonas putida) have been investigated for their potential to utilize by-products as cheap feedstocks. The advantage of these bioprocesses is that a significant amount of PHA can be obtained using renewable carbon sources. The main disadvantage is that the chemical structure of the obtained biopolymer cannot be determined in advance, as is the case with bioprocesses using a conventional carbon source. Polyhydroxyalkanoates are materials that can be used in many fields, such as the medical field (skin grafts, implantable medical devices, scaffolds, drug-controlled release devices), agriculture (for polluted water cleaning), cosmetics and food (biodegradable packaging, gentle biosurfactants with suitable skin for cosmetics), and industry (production of biodegradable biopolymers that replace conventional plastic). Nonetheless, PHA biopolymers continue to be researched and improved and play an important role in various industrial sectors. The properties of this material allow its use as a biodegradable material in the cosmetics industry (for packaging), in the production of biodegradable plastics, or in biomedical engineering, as various prostheses or implantable scaffolds.

Design of experiment techniques for the optimization of chromatographic analysis conditions: A review

Design of experiment (DoE) techniques have been widely used in the field of chromatographic parameters optimization as a valuable tool. A systematic literature review of the available DoE techniques applied to the development of a chromatographic analysis method is presented in this paper. First, the most common available designs and the implementation steps of DoE are comprehensively introduced. Then the studies in recent 10 years for the application of DoE techniques in various chromatographic techniques are discussed, such as capillary electrophoresis, liquid chromatography, gas chromatography, thin-layer chromatography, and high-speed countercurrent chromatography. Current problems and future outlooks are finally given to provide a certain inspiration of research in the application of DoE techniques to the different chromatographic techniques field. This review contributes to a better understanding of the DoE techniques for the efficient optimization of chromatographic analysis conditions, especially for the analysis of complex systems, such as multicomponent drugs and natural products.