Fully Symmetric Cyclodextrin Polycarboxylates: How to Determine Reliable Protonation Constants from NMR Titration Data.

Acid-base properties of cyclodextrins (CDs), persubstituted at C-6 by 3-mercaptopropionic acid, sualphadex (Suα-CD), subetadex (Suß-CD) and sugammadex (Suγ-CD, the antidote of neuromuscular blocking steroids) were studied by 1H NMR-pH titrations. For each CD, the severe overlap in protonation steps prevented the calculation of macroscopic pKa values using the standard data fitting model. Considering the full symmetry of polycarboxylate structures, we reduced the number of unknown NMR parameters in the "Q-fitting" or the novel "equidistant macroscopic" evaluation approaches. These models already provided pKa values, but some of them proved to be physically unrealistic, deceptively suggesting cooperativity in carboxylate protonations. The latter problem could be circumvented by adapting the microscopic site-binding (cluster expansion) model by Borkovec, which applies pairwise interactivity parameters to quantify the mutual basicity-decreasing effect of carboxylate protonations. Surprisingly, only a single averaged interactivity parameter could be calculated reliably besides the carboxylate 'core' microconstant for each CD derivative. The speciation of protonation isomers hence could not be resolved, but the optimized microscopic basicity parameters could be converted to the following sets of macroscopic pKa values: 3.84, 4.35, 4.81, 5.31, 5.78, 6.28 for Suα-CD; 3.82, 4.31, 4.73, 5.18, 5.64, 6.06, 6.54 for Suß-CD and 3.83, 4.28, 4.65, 5.03, 5.43, 5.81, 6.18, 6.64 for Suγ-CD. The pH-dependent charge of these compounds can now be accurately calculated, in support of designing new analytical methods to exploit their charge-dependent molecular recognition such as in cyclodextrin-aided chiral capillary electrophoresis.

Successive complementary model-based experimental designs for parameter estimation of fed-batch bioreactors.

When a dynamic model is used for the description of (fed-)batch bioreactors, it is typical that the model parameters are highly correlated to each other. In this case, it is important to keep the parameter correlation as small as possible to obtain a reliable set of parameter estimates. In this study, we propose an anticorrelation parameter estimation scheme that can be best utilized when a number of different batch experiments are sequentially processed. The scheme iteratively performs parameter estimation and model-based design of experiment (MBDOE) at the beginning and between the batches. The important difference from the existing approaches is that the MBDOE objective is defined according to the system analysis performed a priori, so that each new batch supplements what is lacking from the previous batches combined, in terms of information. The use of the scheme is illustrated on a fed-batch bioreactor model.

A machine learning-based approach for improving plasmid DNA production in Escherichia coli fed-batch fermentations.

Artificial Intelligence (AI) technology is spearheading a new industrial revolution, which provides ample opportunities for the transformational development of traditional fermentation processes. During plasmid fermentation, traditional subjective process control leads to highly unstable plasmid yields. In this study, a multi-parameter correlation analysis was first performed to discover a dynamic metabolic balance among the oxygen uptake rate, temperature, and plasmid yield, whilst revealing the heating rate and timing as the most important optimization factor for balanced cell growth and plasmid production. Then, based on the acquired on-line parameters as well as outputs of kinetic models constructed for describing process dynamics of biomass concentration, plasmid yield, and substrate concentration, a machine learning (ML) model with Random Forest (RF) as the best machine learning algorithm was established to predict the optimal heating strategy. Finally, the highest plasmid yield and specific productivity of 1167.74 mg L-1 and 8.87 mg L-1/OD600 were achieved with the optimal heating strategy predicted by the RF model in the 50 L bioreactor, respectively, which was 71% and 21% higher than those obtained in the control cultures where a traditional one-step temperature upshift strategy was applied. In addition, this study transformed empirical fermentation process optimization into a more efficient and rational self-optimization method. The methodology employed in this study is equally applicable to predict the regulation of process dynamics for other products, thereby facilitating the potential for furthering the intelligent automation of fermentation processes.

Effects of Process and Formulation Parameters on Submicron Polymeric Particles Produced by a Rapid Emulsion-Diffusion Method.

Emulsification-diffusion method is often used to produce polymeric nanoparticles. However, their numerous and/or lengthy steps make it difficult to use widely. Thus, a modified method using solvent blends (miscible/partially miscible in water, 25-100%) as the organic phases to overcome these disadvantages and its design space were investigated. To further simplify the process, no organic/aqueous phase saturation and no water addition after the emulsification step were performed. Biodegradable (PLGA) or pH-sensitive (Eudragit® E100) nanoparticles were robustly produced using low/medium shear stirring adding dropwise the organic phase into the aqueous phase or vice versa. Several behaviors were also obtained: lowering the partially water-miscible solvent ratio relative to the organic phase or the poloxamer-407 concentration; or increasing the organic phase polarity or the polyvinyl alcohol concentration produced smaller particle sizes/polydispersity. Nanoparticle zeta potential increased as the water-miscible solvent ratio increased. Poloxamer-407 showed better performance to decrease the particle size (~50 nm) at low concentrations (≤1%, w/v) compared with polyvinyl alcohol at 1-5% (w/v), but higher concentrations produced bigger particles/polydispersity (≥600 nm). Most important, an inverse linear correlation to predict the particle size by determining the solubility parameter was found. A rapid method to broadly prepare nanoparticles using straightforward equipment is provided.

Data-Driven Development of Predictive Models for Sustained Drug Release.

Mathematical modeling of drug release can aid in the design and development of sustained delivery systems, but the parameter estimation of such models is challenging owing to the nonlinear mathematical structure and complexity and interdependency of the physical processes considered. Highly parameterized models often lead to overfitting, strong parameter correlations, and as a consequence, inaccurate model predictions for systems not explicitly part of the fitting database. Here, we show that an efficient stochastic optimization algorithm can be used not only to find robust estimates of global minima to such complex problems but also to generate metadata that allow quantitative evaluation of parameter sensitivity and correlation, which can be used for further model refinement and development. A practical methodology is described through the analysis of a predictive drug release model on published experimental data sets. The model is then used to design a zeroth-order release profile in an experimental system consisting of an antibody fragment in a poly(lactic-co-glycolic acid) solvent depot, which is validated experimentally. This approach allows rational decision-making when developing new models, selecting models for a specific application, or designing formulations for experimental trials.

Some Health States Are Better Than Others: Using Health State Rank Order to Improve Probabilistic Analyses.

BACKGROUND: Probabilistic sensitivity analyses (PSA) may lead policy makers to take nonoptimal actions due to misestimates of decision uncertainty caused by ignoring correlations. We developed a method to establish joint uncertainty distributions of quality-of-life (QoL) weights exploiting ordinal preferences over health states. METHODS: Our method takes as inputs independent, univariate marginal distributions for each QoL weight and a preference ordering. It establishes a correlation matrix between QoL weights intended to preserve the ordering. It samples QoL weight values from their distributions, ordering them with the correlation matrix. It calculates the proportion of samples violating the ordering, iteratively adjusting the correlation matrix until this proportion is below an arbitrarily small threshold. We compare our method with the uncorrelated method and other methods for preserving rank ordering in terms of violation proportions and fidelity to the specified marginal distributions along with PSA and expected value of partial perfect information (EVPPI) estimates, using 2 models: 1) a decision tree with 2 decision alternatives and 2) a chronic hepatitis C virus (HCV) Markov model with 3 alternatives. RESULTS: All methods make tradeoffs between violating preference orderings and altering marginal distributions. For both models, our method simultaneously performed best, with largest performance advantages when distributions reflected wider uncertainty. For PSA, larger changes to the marginal distributions induced by existing methods resulted in differing conclusions about which strategy was most likely optimal. For EVPPI, both preference order violations and altered marginal distributions caused existing methods to misestimate the maximum value of seeking additional information, sometimes concluding that there was no value. CONCLUSIONS: Analysts can characterize the joint uncertainty in QoL weights to improve PSA and value-of-information estimates using Open Source implementations of our method.

Parameters describing nonequilibrium transport of polycyclic aromatic hydrocarbons through contaminated soil columns: estimability analysis, correlation, and optimization.

The soil and groundwater at former industrial sites polluted by polycyclic aromatic hydrocarbons (PAHs) produce a very challenging environmental issue. The description of PAH transport by means of mathematical models is therefore needed for risk assessment and remediation strategies at these sites. Due to the complexity of release kinetics and transport behavior of the PAHs in the aged contaminated soils, their transport is usually evaluated at the laboratory scale. Transport parameters are then estimated from the experimental data via the inverse method. To better assess the uncertainty of optimized parameters, an estimability method was applied to firstly investigate the information content of experimental data and the possible correlations among parameters in the two-site sorption model. These works were based on the concentrations of three PAHs, Acenaphthene (ACE), Fluoranthene (FLA) and Pyrene (PYR), in the leaching solutions of the experiments under saturated and unsaturated flow conditions. The estimability results showed that the experiment under unsaturated flow conditions contained more information content for estimating four transport parameters than under the saturated one. In addition, whatever the experimental conditions for all three PAHs the fraction of sites with instantaneous sorption, f, was highly correlated with the adsorption distribution coefficient, Kd. The very strong correlation between the two parameters f and Kd suggests that they should not be simultaneously calibrated. Transport parameters were optimized using HYDRUS-1D software with different scenarios based on the estimability analysis results. The optimization results were not always reliable, especially in the case of the experiment under saturated flow conditions because of its low information content. In addition, the estimation of transport parameters became very uncertain if two parameters f and Kd were optimized simultaneously. The findings of the current work can suggest some reasons behind the optimization problems and indicate the type of experimental information additionally needed for parameter identification. To overcome the parameterization issues of PAH non-equilibrium transport, the experimental design, timescale, and model refinement need further improvement. The conclusions presented in this paper are not limited necessarily to PAHs, but may also be relevant to other organic contaminants with similar leaching behavior.