Design Of Pharmaceutical Processes Research Articles

Modeling solid solute solubility in supercritical carbon dioxide by machine learning algorithms using molecular sigma profiles

Supercritical carbon dioxide (ScCO2) is a solvent used in industries due to its non-toxic, non-flammable nature and unique liquid–gas properties. To predict the solubility of different solid solutes in ScCO2 for extraction or pharmaceutical process design, machine learning (ML) has been widely used to correlate the thermodynamics parameters with solubility. This work presents an investigation of the solubility of 116 drug-like solutes (comprising 2791 data points) in ScCO2 using machine learning models. Deep neural network (DNN) and random forest (RF) methods were employed with temperature, pressure, CO2 density, molecular weight, and sigma profile (σ-profile) as input features for predicting solubility. Results indicate strong ML model capabilities with R2 and ALD-x values of DNN being 0.9911 and 0.090 and those of RF being 0.9892 and 0.087, though DNN showed slightly higher errors. The effect of reducing input features of σ-profile on prediction accuracy revealed a negligible increase in error but maintained overall performance. This demonstrates that using a reduced number of σ-profile features offers practical estimation with limited computational resources. This work shows the robust potential of machine learning in accurately predicting the solubility of solids in ScCO2 using σ-profiles.

Simulation-optimization framework for the digital design of pharmaceutical processes using Pyomo and PharmaPy.

The problem of performing model-based process design and optimization in the pharmaceutical industry is an important and challenging one both computationally and in choice of solution implementation. In this work, a framework is presented to directly utilize a process simulator via callbacks during derivative-based optimization. The framework allows users with little experience in translating mechanistic ODEs and PDEs to robust, fully discretized algebraic formulations, required for executing simultaneous equation-oriented optimization, to obtain mathematically guaranteed optima at a competitive solution time when compared with existing derivative-free and derivative-based frameworks. The effectiveness of the framework in accuracy of optimal solution as well as computational efficiency is analyzed on on two case studies: (i) an integrated 2-unit reaction synthesis train used for the synthesis of an anti-cancer active pharmaceutical ingredient, and (ii) a more complex flowsheet representing a common synthesis-purification-isolation train of a pharmaceutical manufacturing processes.

Parametrization of PC-SAFT EoS for Solvents Reviewed for Use in Pharmaceutical Process Design: VLE, LLE, VLLE, and SLE Study

Parametrization of PC-SAFT EoS for Solvents Reviewed for Use in Pharmaceutical Process Design: VLE, LLE, VLLE, and SLE Study

Correlation and prediction of small to large sized pharmaceuticals solubility, and crystallization in binary and ternary mixed solvents using the UNIQUAC-SAC model

The recently reported UNIversal QUAsiChemical Segment Activity coefficient (UNIQUAC-SAC) model [developed by Haghtalab and Yousefi Seyf Ind. Eng. Chem. Res. 2015, 54, 8611] provides a practical thermodynamic framework to be used in VLE, LLE, and SLE calculations. The UNIQUAC-SAC model has the advantage of being independent of area (q) and volume (r) structural parameters used in the combinatorial part. While the UNIFAC or UNIFAC-DMD could not apply to the 47% (44 of 94) of the studied molecules because of the undefined groups. Here, the numbers of solvents with identified segment numbers were extended from 82 to 130 with a slight refinement to the previous values. The model parameters obtained via a large consistent set of VLE (isobaric and isothermal), and LLE experimental data. Also, the model was tested with pharmaceutical solubility experimental data in pure (94 solutes with 6210 solubility data), binary, and ternary solvents. The model provides a robust correlation of pharmaceuticals solubility in a mono-solvent and robust prediction of pharmaceuticals solubility in mixed solvents. The UNIQUAC-SAC model was successfully evaluated in solvent screening in cooling crystallization of ibuprofen and valsartan in both pure and binary solvents mixture (combined cooling and anti-solvent crystallization). The model with 130 solvents as a robust database provides a useful and practical thermodynamic tool in the conceptual design of pharmaceutical processes. A MATLAB based graphical user interface (GUI), finally, was developed to be used in the conceptual segment numbers regression procedure.

Density and viscosity of binary mixtures of diethyl oxalate with ethanol, ethyl acetate, tetrahydrofuran, and toluene

ABSTRACT This study presents the densities and viscosities of four binary liquid mixtures containing diethyl oxalate (DEO) with the organic solvents ethanol, ethyl acetate (EA), tetrahydrofuran (THF), and toluene at temperatures ranging from 303.15 to 323.15 K and at atmospheric pressure. DEO is widely used in the pharmaceutical industry as an intermediate or reaction reagent for synthesizing active pharmaceutical ingredients. The measured thermophysical properties are important for pharmaceutical process design and development. A vibrating tube digital density meter and a capillary viscometer were used for density and viscosity measurements, respectively. According to the measurement data, the excess molar volumes (VE ) and viscosity deviations ( ) of the binary mixtures were calculated and satisfactorily correlated with the four-parameter Redlich–Kister equation. Ethanol or THF addition to DEO led to a positive deviation of VE , whereas EA or toluene addition to DEO led to a negative deviation. These results indicated the molecular packing of DEO with EA and toluene was more efficient than that with ethanol and THF. By contrast, the values of the four binary mixtures were negative, indicating that the viscosity was markedly lower than that of an ideal mixture.

Modelling chemical kinetics of a complex reaction network of active pharmaceutical ingredient (API) synthesis with process optimization for benzazepine heterocyclic compound

Pharmaceutical process design and engineering increasingly recognizes the role of chemical engineering in optimizing the productivity of active pharmaceutical ingredient (API) as well as their scale-up. In the present study, this concept is outlined through the kinetic model of numerous parallel and serial reactions that proceed via a two-step benzazepine heterocyclic compound synthesis, introducing 1-(2-bromoethyl)-4-chlorobenzene (4CEBr) and allylamine (AA) as first step reactants, N-(4-chlorophenethyl)prop-2-en-1-aminium chloride (4CPAL) as intermediate (second step reactant), and (1R)-8-chloro-1-methyl-2,3,4,5-tetrahydro-1H-3-benzazepine (lorcaserin) as the desired product, with the second step heterogeneously/homogeneously (negligible mass transfer resistance) catalysed by AlCl3 as Lewis acid. In addition to these, a plethora of by-products, formed through various mechanisms ranging from eliminations, nucleophilic and electrophilic substitutions and additions, Friedel–Crafts reactions and others, was identified and quantified by nuclear magnetic resonance (NMR), high-performance liquid chromatography coupled with mass spectroscopy (HPLC–MS) and gas chromatography. A large set of experiments was executed for both the first and second step, varying temperature (20–60°C for the first and 100–120°C for the second step), reactant and catalyst concentrations, as well as the presence of the first-step reactant (4CEBr and AA) in the second, whereas the stepwise multi-regression modelling with consequent sensitivity analysis ensued. Model optimization indicated that conversions as high as 91% may be achieved in the second step; nonetheless, that a trade-off between yield and productivity has to be considered. An efficient optimization of synthesis steps facilitates further downstream separation, as well as streamlines process intensification when proceeding to the crystallization of API polymorph.

The First Scale-Up Production of Theranostic Nanoemulsions.

Theranostic nanomedicines are a promising new technological advancement toward personalized medicine. Although much progress has been made in pre-clinical studies, their clinical utilization is still under development. A key ingredient for successful theranostic clinical translation is pharmaceutical process design for production on a sufficient scale for clinical testing. In this study, we report, for the first time, a successful scale-up of a model theranostic nanoemulsion. Celecoxib-loaded near-infrared-labeled perfluorocarbon nanoemulsion was produced on three levels of scale (small at 54 mL, medium at 270 mL, and large at 1,000 mL) using microfluidization. The average size and polydispersity were not affected by the equipment used or production scale. The overall nanoemulsion stability was maintained for 90 days upon storage and was not impacted by nanoemulsion production scale or composition. Cell-based evaluations show comparable results for all nanoemulsions with no significant impact of nanoemulsion scale on cell toxicity and their pharmacological effects. This report serves as the first example of a successful scale-up of a theranostic nanoemulsion and a model for future studies on theranostic nanomedicine production and development.

Active pharmaceutical ingredient (API) production involving continuous processes – A process system engineering (PSE)-assisted design framework

A systematic framework is proposed for the design of continuous pharmaceutical manufacturing processes. Specifically, the design framework focuses on organic chemistry based, active pharmaceutical ingredient (API) synthetic processes, but could potentially be extended to biocatalytic and fermentation-based products. The method exploits the synergic combination of continuous flow technologies (e.g., microfluidic techniques) and process systems engineering (PSE) methods and tools for faster process design and increased process understanding throughout the whole drug product and process development cycle. The design framework structures the many different and challenging design problems (e.g., solvent selection, reactor design, and design of separation and purification operations), driving the user from the initial drug discovery steps – where process knowledge is very limited – toward the detailed design and analysis. Examples from the literature of PSE methods and tools applied to pharmaceutical process design and novel pharmaceutical production technologies are provided along the text, assisting in the accumulation and interpretation of process knowledge. Different criteria are suggested for the selection of batch and continuous processes so that the whole design results in low capital and operational costs as well as low environmental footprint. The design framework has been applied to the retrofit of an existing batch-wise process used by H. Lundbeck A/S to produce an API: zuclopenthixol. Some of its batch operations were successfully converted into continuous mode, obtaining higher yields that allowed a significant simplification of the whole process. The material and environmental footprint of the process – evaluated through the process mass intensity index, that is, kg of material used per kg of product – was reduced to half of its initial value, with potential for further reduction. The case-study includes reaction steps typically used by the pharmaceutical industry featuring different characteristic reaction times, as well as L–L separation and distillation-based solvent exchange steps, and thus constitutes a good example of how the design framework can be useful to efficiently design novel or already existing API manufacturing processes taking advantage of continuous processes.

MULTISCALE FAULT DETECTION AND DIAGNOSIS IN FED-BATCH FERMENTATION

The importance of the FDA PAT guidelines in pharmaceutical process design space can be influenced by the introduction of robust process malfunction and senor fault detection and diagnosis tools. The paper compares a multi-scale multi-block modelling approach with conventional multiway PCA approaches for batch process monitoring. A benchmark penicillin fermentation simulation is used to evaluate the two methodologies. Contributions plots with confidence bounds enhance the fault diagnosis potential of the approaches studied. The methodology is in the process of being evaluated in fermentation and batch cooling crystallisation.

Extension of Nonrandom Two-Liquid Segment Activity Coefficient Model for Electrolytes

The nonrandom two-liquid segment activity coefficient model of Chen and Song (Ind. Eng. Chem. Res. 2004, 43, 8354) has shown to be a simple and practical tool for chemists and engineers to correlate and estimate solubilities of organic nonelectrolytes in support of chemical and pharmaceutical process design. In this paper, the model is extended for the computation of ionic activity coefficients and solubilities of electrolytes, organic and inorganic, in common solvents and solvent mixtures. In addition to the three types of molecular parameters defined for organic nonelectrolytes, i.e., hydrophobicity X, polarity Y, and hydrophilicity Z, an electrolyte parameter, E, is introduced to characterize both local and long-range ion−ion and ion−molecule interactions attributed to ionized segments of electrolytes. Successful representations of mean ionic activity coefficients and solubilities of electrolytes, inorganic and organic, in aqueous and nonaqueous solvents are presented.

Solubility Modeling with a Nonrandom Two-Liquid Segment Activity Coefficient Model

A segment contribution activity coefficient model, derived from the polymer nonrandom two-liquid model, is proposed for fast, qualitative estimation of the solubilities of organic nonelectrolytes in common solvents. Conceptually, the approach suggests that one account for the liquid nonideality of mixtures of complex pharmaceutical molecules and small solvent molecules in terms of interactions between three pairwise interacting conceptual segments: hydrophobic segment, hydrophilic segment, and polar segment. In practice, these conceptual segments become the molecular descriptors used to represent the molecular surface characteristics of each solute and solvent molecule. The treatment results in component-specific molecular parameters: hydrophobicity X, polarity Y, and hydrophilicity Z. Once the molecular parameters are identified from experimental data for common solvents and solute molecules, the model offers a simple and practical thermodynamic framework to estimate solubilities and to perform other phase equilibrium calculations in support of pharmaceutical process design.