Step-By-Step Standardization of the Bottom-Up Semi-Automated Nanocrystallization of Pharmaceuticals: A Quality By Design and Design of Experiments Joint Approach.

Nanosized drug crystals have been reported with enhanced apparent solubility, bioavailability, and therapeutic efficacy compared to microcrystal materials, which are not suitable for parenteral administration. However, nanocrystal design and development by bottom-up approaches are challenging, especially considering the non-standardized process parameters in the injection step. This work aims to present a systematic step-by-step approach through Quality-by-Design (QbD) and Design of Experiments (DoE) for synthesizing drug nanocrystals by a semi-automated nanoprecipitation method. Curcumin is used as a drug model due to its well-known poor water solubility (0.6 µg mL-1, 25 °C). Formal and informal risk assessment tools allow identifying the critical factors. A fractional factorial 24-1 screening design evaluates their impact on the average size and polydispersity of nanocrystals. The optimization of significant factors is done by a Central Composite Design. This response surface methodology supports the rational design of the nanocrystals, identifying and exploring the design space. The proposed joint approach leads to a reproducible, robust, and stable nanocrystalline preparation of 316 nm with a PdI of 0.217 in compliance with the quality profile. An orthogonal approach for particle size and polydispersity characterization allows discarding the formation of aggregates. Overall, the synergy between advanced data analysis and semi-automated standardized nanocrystallization of drugs is highlighted.

Unidirectional Transmission Metasurfaces with Topological Continuity Generated from High-dimensional Design Space.

With the growing demand for nanodevices, there is a concerted effort to improve the design flexibility of nanostructures, thereby expanding the capabilities of nanophotonic devices. In this work, a Laplacian-weighted binary search (LBS) algorithm is proposed to generate a unidirectional transmission metasurface from a high-dimensional design space, offering an increased degree of design freedom. The LBS algorithm incorporates topological continuity based on the Laplacian, effectively circumventing the common issue of high structural complexity in designing high-dimensional nanostructures. As a result, metasurfaces developed using the LBS algorithm in a high-dimensional design space exhibit reduced complexity, which is advantageous for experimental fabrication. An all-dielectric metasurface with unidirectional transmission, designed from the high-dimensional space using the LBS method, demonstrated the successful application of these design principles in experiments. The metasurface exhibits high optical performance on unidirectional transmission in measurements by a high-resolution angle-resolved micro-spectra system, achieving forward transmissivity above 90% (400-700 nm) and back transmissivity below 20% (400-500 nm) within the targeted wavelength range. This work provides a feasible approach for advancing high-dimensional metasurface applications, as the LBS design method takes into account topological continuity during experimental processing. Compared to traditional direct binary search (DBS) methods, the LBS method not only improves information processing efficiency but also maintains the topological continuity of structures. Beyond unidirectional transmission, the LBS-based design method has generality and flexibility to accommodate almost all physical scenarios in metasurface design, enabling a multitude of complex functions and applications.

Pillars of theoretical biology: "Biochemical systems analysis, I, II and III".

Michael Savageau's Biochemical Systems Analysis I, II, IIIpapers, published in volumes 25 and 26 of the journal,kickstarted a research programme that originated many of the core concepts and tools of Systems Biology. This article briefly summarizes these papers anddiscusses the most relevant developments in Biochemical Systems Theory since their publication.

Current Approaches to Design Space Development and Regulatory Applications for Drug Products: Findings from the IQ Utilization of Design Space for Filings Working Group Survey.

PURPOSE: The concept of a Design Space (DSp) was introduced in ICH Q8 as a tool within the quality-by-design (QbD) approach to pharmaceutical development with the intent of being globally applicable. However, there appears to be variance in the regulatory agency expectations in pharmaceutical product filing and implementation of DSp. This paper presents some of the current industry perspective on design space. METHODS: The Utilization of Design Space for Filings (UDSpF) Working Group in the Innovation and Quality (IQ) Consortium conducted a survey to establish a baseline for the current understanding of DSp among IQ member companies and assess the similarities and/or differences in strategies when filing a DSp. The survey focused on how IQ member companies approach DSp development, the primary drivers for the DSp, the presentation of the DSp in the filing, DSp verification and the benefits and flexibility anticipated and/or realized. RESULTS: A total of 14 responses were received and analyzed representing a small sample size but a large proportion of the innovator industry/large pharmaceutical companies. The survey results revealed that DSp is not yet a commonplace for small molecule drug products and may not even be utilized as much in large molecule drug products. The benefits of DSp, with respect to enhanced process understanding, are well understood by the sponsors; however, the benefits of filed DSp with respect to manufacturing flexibility are not fully realized in the commercial lifecycle of the product. There are also challenges in gaining consistent buy-in/value proposition for DSp among cross-functional teams within organizations. CONCLUSIONS: There are still gaps in design space implementation for its full benefit in the pharmaceutical industry. The WG has presented a unified view from member companies on the approach to DSp considering when/where the DSp experiments are conducted and on the extent of the DSp development proposed in a dossier.

Flare: An FPGA-Based Full Precision Low Power CNN Accelerator with Reconfigurable Structure.

Convolutional neural networks (CNNs) have significantly advanced various fields; however, their computational demands and power consumption have escalated, posing challenges for deployment in low-power scenarios. To address this issue and facilitate the application of CNNs in power constrained environments, the development of dedicated CNN accelerators is crucial. Prior research has predominantly concentrated on developing low precision CNN accelerators using code generated from high-level synthesis (HLS) tools. Unfortunately, these approaches often fail to efficiently utilize the computational resources of field-programmable gate arrays (FPGAs) and do not extend well to full precision scenarios. To overcome these limitations, we integrate vector dot products to unify the convolution and fully connected layers. By treating the row vector of input feature maps as the fundamental processing unit, we balance processing latency and resource consumption while eliminating data rearrangement time. Furthermore, an accurate design space exploration (DSE) model is established to identify the optimal design points for each CNN layer, and dynamic partial reconfiguration is employed to maximize each layer's access to computational resources. Our approach is validated through the implementation of AlexNet and VGG16 on 7A100T and ZU15EG platforms, respectively. We achieve an average convolutional layer throughput of 28.985 GOP/s and 246.711 GOP/s for full precision. Notably, the proposed accelerator demonstrates remarkable power efficiency, with a maximum improvement of 23.989 and 15.376 times compared to current state-of-the-art FPGA implementations.

Quality-by-design driven approach in the formulation of an anti-ulcer and gastro-protective oral suspension.

OBJECTIVE: This work aims to present a Quality-by-Design (QbD) step-by-step methodology to formulate anti-ulcer and gastro-protective oral suspensions. METHODS: Sucralfate was used as a drug model. The Quality Target Product Profile was established early during preformulation. Viscosity, resuspendability, pH, and density were assessed through the screening of several suspension platforms based on different prototype compositions. A compatibility study between the active pharmaceutical ingredient and the excipients was performed by thermal analysis and infrared spectroscopy. An Ishikawa fishbone diagram and Failure Mode and Effect Analysis were employed to identify the Critical Material Attributes (CMAs), Critical Process Parameters (CPPs), and Critical Quality Attributes (CQAs). CMAs' and CPPs' impact on identified CQAs was further assessed through a 22 full factorial experimental design at normal conditions after manufacture and one month at super-accelerated stress conditions. Results: The lead prototype exhibited no physicochemical incompatibilities. The risk assessment tools revealed that the concentration of the wetting agent and the total concentration of thickening agents represented critical factors for the quality profile of the preparation in terms of viscosity. The optimized formulation comprising 1.125 w/v% total concentration of Natrosol 250 HX and Avicel RC 591 exhibited an enhanced performance according to the established profile. CONCLUSIONS: The analytical and physicochemical tests showed the robustness and compliance of the final preparation with the quality profile. The proposed step-by-step methodology based on QbD, Design of Experiments, and Quality Risk Management presented in our research holds practical implications for local industries and formulation scientists involved in the development of oral suspensions.

Design space determination to optimize DNA complexation and full capsid formation in transient rAAV manufacturing.

Recombinant adeno-associated virus (rAAV) vectors are a promising platform for in vivo gene therapies. However, cost-effective, well-characterized processes necessary to manufacture rAAV therapeutics are challenging to develop without an understanding of how process parameters (PPs) affect rAAV product quality attributes (PQAs). In this work, a central composite orthogonal experimental design was employed to examine the influence of four PPs for transient transfection complex formation (polyethylenimine:DNA [PEI:DNA] ratio, total DNA/cell, cocktail volume, and incubation time) on three rAAV PQAs related to capsid content (vector genome titer, vector genome:capsid particle ratio, and two-dimensional vector genome titer ratio). A regression model was established for each PQA using partial least squares, and a design space (DS) was defined in which Monte Carlo simulations predicted < 1% probability of failure (POF) to meet predetermined PQA specifications. Of the three PQAs, viral genome titer was most strongly correlated with changes in complexation PPs. The DS and acceptable PP ranges were largest when incubation time and cocktail volume were kept at mid-high setpoints, and PEI:DNA ratio and total DNA/cell were at low-mid setpoints. Verification experiments confirmed model predictive capability, and this work establishes a framework for studying other rAAV PPs and their relationship to PQAs.

Reaction kinetics determination and uncertainty analysis for the synthesis of the cancer drug lomustine.

Fast and reliable model development frameworks are required to support current trends in modernization of pharmaceutical processing, promoting the use of digital platforms to assist process design and operation. In this work, we use a parameter estimation framework built into the PharmaPy library to determine rate parameters and uncertainty regions of different mechanistic and semi-empirical kinetic expressions for the synthesis of the drug lomustine. The parameter estimation procedure was complemented by identifiability analysis, resulting in simplified reaction mechanisms. Comparison of parameters and their uncertainty in process design was demonstrated through design space analysis, showing important differences in model prediction and the extent of their corresponding design spaces. The results of this work can serve to analyze lomustine manufacturing processes that include separation and isolation steps, where parametric sensitivity is expected to propagate along the manufacturing line and impact process feasible operation, and attainment of critical quality attributes of the product.

Optimization of dropping process of Xuesaitong dropping pills based on quality by design concept and machine vision.

OBJECTIVE: The drooping process of the Xuesaitong dropping pills (XDPs) was optimized based on quality by design concept. Meanwhile, a machine vision (MV) technology was creatively introduced in this study to predict the critical quality attributes (CQAs) rapidly and accurately. SIGNIFICANCE: This study improves the understanding of dropping process, and has reference value for the guidance of pharmaceutical process research and industrial production. METHODS: The study mainly consisted of three stages: the first stage involved the prediction model to establish and evaluate the CQAs, and the second stage involved assessing the quantitative relationships between critical process parameters (CPPs) and CQAs by the mathematical models that were established through the Box-Behnken experimental design. Finally, a probability-based design space for the dropping process was calculated and verified according to the qualification criteria of each quality attribute. RESULTS: The results show that the prediction accuracy of the random forest (RF) model was high and met the analysis requirements, and the CQAs of dropping pills can meet the standard by running in the design space. CONCLUSION: The MV technology developed in this study can be applied to the optimization process of the XDPs. In addition, the operation in the design space can not only ensure the quality of XDPs to meet the criteria, but also help to improve the consistency of XDPs.

Determination of Response Factors for Analytes Detected during Migration Studies, Strategy and Internal Standard Selection for Risk Minimization.

Migration studies are one of the few domains of pharmaceutical analysis employing wide-scope screening methodologies. The studies involve the detection of contaminants within pharmaceutical products that arise from the interaction between the formulation and materials. Requiring both qualitative and quantitative data, the studies are conducted using Liquid Chromatography or Gas Chromatography coupled to a mass spectrometer (LC-MS and GC-MS). While mass spectrometry allows wide-scope analyte detection and identification at the very low Analytical Evaluation Threshold (AET) levels used in these studies, MS detectors are far from "universal response" detectors. Regulation brings the application of uncertainty factors into the picture to limit the risk of potential analytes detected escaping report and further evaluation; however, whether the application of a default value can cover any or all relevant applications is still debatable. The current study evaluated the response of species usually detected in migration studies, generating a suitable representative sample, analyzing said species, and creating a strategy and evaluation mechanism for acceptable classification of the detected species. Incorporating novel methodologies, i.e., Design of Experiments (DoE) for Design Space generation, the LC-MS-based methodology is also evaluated for its robustness in changes performed.

Phenotype-centric modeling for rational metabolic engineering.

Phenotype-centric modeling enables a paradigm shift in the analysis of mechanistic models. It brings the focus to a network's biochemical phenotypes and their relationship with measurable traits (e.g., product yields, system dynamics, signal amplification factors, etc.) and away from computationally intensive simulation-centric modeling. Here, we explore applications of this new modeling strategy in the field of rational metabolic engineering using the amorphadiene biosynthetic network as a case study. This network has previously been studied using a mechanistic model and the simulation-centric strategy, and thus provides an excellent means to compare and contrast results obtained from these two very different strategies. We show that the phenotype-centric strategy, without values for the parameters, not only identifies beneficial intervention strategies obtained with the simulation-centric strategy, but it also provides an understanding of the mechanistic context for the validity of these predictions. Additionally, we propose a set of hypothetical strains with the potential to outperform reported production strains and to enhance the mechanistic understanding of the amorphadiene biosynthetic network. Further, we identify the landscape of possible intervention strategies for the given model. We believe that phenotype-centric modeling can advance the field of rational metabolic engineering by enabling the development of next generation kinetics-based algorithms and methods that do not rely on a priori knowledge of kinetic parameters but allow a structured, global analysis of system design in the parameter space.

Application of the "Method Operable Design Region" (MODR) approach for the development of a UHPLC method for the assay and purity determination of risperidone in risperidone drug substance and other formulations.

To understand the role of analytics in drug development, regulatory bodies also started using the approach of Quality by Design (QbD) during analytical method developments. The present study deals with the development of the "Method Operable Design Region" for assay and purity determination of risperidone in risperidone drug substance and formulations usingy UHPLC. Five different column chemistries, five different pH buffers, oven temperatures from 25 to 45°C, and different organic modifier composition, column lengths and flow rates were studied and statistically evaluated using Fusion QbD software. The final method parameters were selected by performing multivariable changes in a single run and evaluated using the Monte Carlo simulation approach. The uniqueness of this method is that it is mass compatible, a total of 10 peaks are separated within a short run time of 12.0 min and it uses a "Platforming approach", which means the use of a single method for testing the drug substance, different strengths of a drug product and different formulations. The same method can be also used for the determination of the preservative (benzoic acid) in risperidone 1 mg/ml oral solution. The use of the QbD approach is aligned with the US Pharmacopeia <1220>, BP supplementary chapter 2022 and the International Conference on Harmonization Q14 guidelines for life cycle management of analytical methods.

Design Space Exploration of Clustered Sparsely Connected MPSoC Platforms.

Heterogeneous multiprocessor platforms are the foundation of systems that require high computational power combined with low energy consumption, like the IoT and mobile robotics. In this paper, we present five new algorithms for the design space exploration of platforms with elements grouped in clusters with very few connections in between, while these platforms have favorable electric properties and lower production costs, the limited interconnectivity and inability of heterogeneous platform elements to execute all types of tasks, significantly decrease the chance of finding a feasible mapping of application to the platform. We base the new algorithms on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) meta-heuristic and the previously published SDSE mapping algorithm designed for fully interconnected multiprocessor platforms. With the aim to improve the chance of finding feasible solutions for sparsely connected platforms, we have modified the parts of the search process concerning the penalization of infeasible solutions, chromosome decoding, and mapping strategy. Due to the lack of adequate existing benchmarks, we propose our own synthetic benchmark with multiple application and platform models, which we believe can be easily extended and reused by other researchers for further studying this type of platform. The experiments show that four proposed algorithms can find feasible solutions in 100% of test cases for platforms with dedicated clusters. In the case of tile-like platforms, the same four algorithms show an average success rate of 60%, with one algorithm going up to 84%.

Implementing the Design of Experiments (DoE) Concept into the Development Phase of Orodispersible Minitablets (ODMTs) Containing Melatonin.

Development of orodispersible minitablets (ODMTs) requires consideration of aspects related to small dimensions, while ensuring short disintegration time with sufficient mechanical stability. In order to meet these and other critical quality attributes (CQAs), quality by design is encouraged. According to this approach, formulation and compression process factors were systematically studied using design of experiments (Plackett-Burman for screening purposes, full and fractional factorial design for in-depth characterization) to understand their influence on CQAs of orodispersible minitablets containing melatonin. Mathematical models describing the relationships between processing variables and attributes such as resistance to crushing and disintegration time were successfully developed, characterized by high coefficients of determination (R2adj = 0.90-0.97) and prediction errors in the range (+2.4 to -10.8%). In conclusion, based on these models, the design space was created for melatonin ODMTs, ensuring the product's quality and process robustness. Moreover, the study demonstrated the suitability of texture analysis as an alternative to compendial measurement methods of resistance to crushing and disintegration time. Graphical abstract.

QbD Approach towards Robust Design Space for Flutamide/PiperineSelf-Emulsifying Drug Delivery System with Reduced Liver Injury.

Flutamide which is used to treat prostate cancer and other diseases induces liver damage during and after the therapy. The aim of this study was to develop a flutamide/piperineco-loaded self-emulsifying drug delivery system (FPSEDDS) to inhibit flutamide-induced liver injury by utilizing piperine as a metabolic inhibitor. The development of SEDDS was carried out following a quality by design (QbD) approach. The risk assessment study was performed to identify critical quality attributes (CQAs) and critical material attributes (CMAs)/critical process parameters (CPPs). I-optimal mixture design was executed with three CMAs as the independent variables and CQAs as the dependable variables. The effectiveness of optimized SEDDS to circumvent flutamide-induced hepatotoxicity was assessed in mice. The numerical optimization suggested an optimal formulation with a desirability value of 0.621, using CQAs targets as optimization goals with 95% prediction intervals (α = 0.05). The optimal formulation exhibited the grade A SEDDS characteristics with the guarantee of high payloads in self-formed oily droplets. The design space was also obtained from the same optimization goals. All CQA responses of verification points were found within the 95% prediction intervals of the polynomial models, indicating a good agreement between actual versus predicted responses within the design space. These obtained responses also passed CQAs acceptance criteria. Finally, hematoxylin-eosin staining revealed the minimal flutamide-induced hepatotoxicity from the optimal SEDDS formulation as compared to the control and flutamide/piperine normal suspension. We demonstrate that the piperine containing optimized SEDDS formulation developed by QbD significantly reduces the flutamide-induced liver injury in mice.

[Prediction of formability of flavonoid extract granules by dry granulation based on design space and RBFNN].

In this paper, a flavonoid extract powder properties-process parameters-granule forming rate prediction model was constructed based on design space and radial basis function neural network(RBFNN) to predict the formability of flavonoid extract gra-nules. Box-Behnken experimental design was employed to explore the mathematical relationships between critical process parameters and quality attributes. The design space of critical process parameters was developed, and the accuracy of the design space was verified by Monte Carlo method(MC). Design Expert 10 was used for Box-Behnken design and mixture design. Scutellariae Radix mixed powder was prepared and its powder properties were measured. The mixed powder was then subjected to dry granulation and the granule forming rate was determined. The correlations between powder properties were analyzed by variance influence factor(VIF), and principal component analysis(PCA) was performed for the factors with strong collinearity. In this way, a prediction model of powder properties-process parameters-granule forming rate was established based on RBFNN, the accuracy of which was evaluated with examples. The results showed that the model had a good predictive effect on the granule forming rate, with the average relative error of 1.04%. The predicted value and the measured value had a high degree of fitting, which indicated that model presented a good prediction ability. The prediction model established in this study can provide reference for the establishment of quality control methods for Chinese medicinal preparations with similar physical properties.

Micro Freeze-Dryer and Infrared-Based PAT: Novel Tools for Primary Drying Design Space Determination of Freeze-Drying Processes.

PURPOSE: Present (i) an infrared (IR)-based Process Analytical Technology (PAT) installed in a lab-scale freeze-dryer and (ii) a micro freeze-dryer (MicroFD®) as effective tools for freeze-drying design space calculation of the primary drying stage. METHODS: The case studies investigated are the freeze-drying of a crystalline (5% mannitol) and of an amorphous (5% sucrose) solution processed in 6R vials. The heat (Kv) and the mass (Rp) transfer coefficients were estimated: tests at 8, 13 and 26 Pa were carried out to assess the chamber pressure effect on Kv. The design space of the primary drying stage was calculated using these parameters and a well-established model-based approach. The results obtained using the proposed tools were compared to the ones in case Kv and Rp were estimated in a lab-scale unit through gravimetric tests and a thermocouple-based method, respectively. RESULTS: The IR-based method allows a non-gravimetric estimation of the Kv values while with the micro freeze-dryer gravimetric tests require a very small number of vials. In both cases, the obtained values of Kv and Rp, as well as the resulting design spaces, were all in very good agreement with those obtained in a lab-scale unit through the gravimetric tests (Kv) and the thermocouple-based method (Rp). CONCLUSIONS: The proposed tools can be effectively used for design space calculation in substitution of other well-spread methods. Their advantages are mainly the less laborious Kv estimation process and, as far as the MicroFD® is concerned, the possibility of saving time and formulation material when evaluating Rp.

Development of an HPLC-MS method for the determination of four terpene trilactones in Ginkgo biloba leaf extract via quality by design.

Previously reported HPLC-evaporative light scattering detection methods for terpene trilactone determination in Ginkgo biloba leaf extract (EGBL) have complicated sample preparation steps and are time-consuming. Thus, in this work, an HPLC-MS method for the determination of terpene trilactones in EGBL was developed with a novel analytical quality by design approach to provide robust and simple measurements. For this purpose, analytical target profiles and systematic risk analyses were performed to identify potential critical method attributes and critical method parameters. After screening experiments, a Box-Behnken design approach was utilized to investigate the relationships between critical method attributes and critical method parameters. A hypercube design space obtained by a Monte Carlo method was used for choosing the analytical control strategy. Then, verification experiments were performed within the design space, and the models were found to be accurate. After that, the optimized method was verified and successfully used for quality control analysis of EGBL from different manufacturers, and the results were almost the same as those determined by HPLC-evaporative light scattering detection. To our knowledge, this is the first study to establish a robust HPLC-MS method for determination of terpene trilactones in EGBL based on a novel analytical quality by design concept, which can improve the quality control of commercial EGBL.

Mathematical modeling and digital design of an intensified filtration-washing-drying unit for pharmaceutical continuous manufacturing.

This paper introduces a comprehensive mathematical model of a novel integrated filter-dryer carousel system, designed for continuously filtering, washing and drying a slurry stream into a crystals cake. The digital twin includes models for dead-end filtration, cake washing and convective cake drying, based on dynamic multi-component mass, energy and momentum balances. For set of feed conditions and control inputs, the model allows tracking the solvents and impurities content in the cake (critical quality attributes, CQAs) throughout the whole process. The model parameters were identified for the isolation of paracetamol from a multi-component slurry, containing a non-volatile impurity. The calibrated model was used for identifying the probabilistic design space and maximum throughput for the process, expressing the combinations of the carousel feed conditions and control inputs for which the probability of meeting the target CQAs is acceptable.

Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems.

The introduction of various networks into automotive cyber-physical systems (ACPS) brings great challenges on security protection of ACPS functions, the auto industry recommends to adopt the hardware security module (HSM)-based multicore ECU to secure in-vehicle networks while meeting the delay constraint. However, this approach incurs significant hardware cost. Consequently, this paper aims to reduce security enhancing-related hardware cost by proposing two efficient design space exploration (DSE) algorithms, namely, stepwise decreasing-based heuristic algorithm (SDH) and interference balancing-based heuristic algorithm (IBH), which explore the task assignment, task scheduling, and message scheduling to minimize the number of required HSMs. Experiments on both synthetical and real data sets show that the proposed SDH and IBH are superior than state-of-the-art algorithm, and the advantage of SDH and IBH becomes more obvious as the increase about the percentage of security-critical tasks. For synthetic data sets, the hardware cost can be reduced by 61.4% and 45.6% averagely for IBH and SDH, respectively; for real data sets, the hardware cost can be reduced by 64.3% and 54.4% on average for IBH and SDH, respectively. Furthermore, IBH is better than SDH in most cases, and the runtime of IBH is two or three orders of magnitude smaller than SDH and state-of-the-art algorithm.