An Optimization Approach Coupling Preprocessing with Model Regression for Enhanced Chemometrics

Chemometric methods are broadly used in the chemical and biochemical sectors. Typically, derivation of a regression model follows data preprocessing in a sequential manner. Yet, preprocessing can significantly influence the regression model and eventually its predictive ability. In this work, we investigate the coupling of preprocessing and model parameter estimation by incorporating them simultaneously in an optimization step. Common model selection techniques rely almost exclusively on the performance of some accuracy metric, yet having a quantitative metric for model robustness can prolong model up-time. Our approach is applied to optimize for model accuracy and robustness. This requires the introduction of a novel mathematical definition for robustness. We test our method in a simulated set up and with industrial case studies from multivariate calibration. The results highlight the importance of both accuracy and robustness properties and illustrate the potential of the proposed optimization approach toward automating the generation of efficient chemometric models.

An optimization-based model discrimination framework for selecting an appropriate reaction kinetic model structure during early phase pharmaceutical process development

A model discrimination workflow to develop fit for purpose kinetic models of new pharmaceutical compounds in early stages of drug development involving complex reaction networks with limited prior information and provision to run new experiments.

Hybrid QSPR models for the prediction of the free energy of solvation of organic solute/solvent pairs

Due to the importance of the Gibbs free energy of solvation in understanding many physicochemical phenomena, including lipophilicity, phase equilibria and liquid-phase reaction equilibrium and kinetics, there is a need for predictive models that can be applied across large sets of solvents and solutes. In this paper, we propose two quantitative structure property relationships (QSPRs) to predict the Gibbs free energy of solvation, developed using partial least squares (PLS) and multivariate linear regression (MLR) methods for 295 solutes in 210 solvents with total number of data points of 1777. Unlike other QSPR models, the proposed models are not restricted to a specific solvent or solute. Furthermore, while most QSPR models include either experimental or quantum mechanical descriptors, the proposed models combine both, using experimental descriptors to represent the solvent and quantum mechanical descriptors to represent the solute. Up to twelve experimental descriptors and nine quantum mechanical descriptors are considered in the proposed models. Extensive internal and external validation is undertaken to assess model accuracy in predicting the Gibbs free energy of solvation for a large number of solute/solvent pairs. The best MLR model, which includes three solute descriptors and two solvent properties, yields a coefficient of determination (R2) of 0.88 and a root mean squared error (RMSE) of 0.59 kcal mol-1 for the training set. The best PLS model includes six latent variables, and has an R2 value of 0.91 and a RMSE of 0.52 kcal mol-1. The proposed models are compared to selected results based on continuum solvation quantum chemistry calculations. They enable the fast prediction of the Gibbs free energy of solvation of a wide range of solutes in different solvents.

Multivariate wavelet texture analysis for pharmaceutical solid product characterization

The application of multivariate wavelet texture analysis (MWTA) is presented and discussed as it is applied to three different types of pharmaceutical materials: (a) tablet cores, (b) wet granules and (c) controlled release tablets. The application of MWTA is initially proposed as a quantitative replacement to the human visual judgment of the textural appearance of the different materials. In all cases, the metrics obtained with MWTA agree with visual assessment on the progression of textural features such as erosion and surface roughness. This work further demonstrates that MWTA also represents a useful tool to increase the understanding of the manufacturing process, as it provides diagnostics to relate process parameters with textural features of the material that are difficult or costly to measure otherwise (such as granule size for wet material or surface appearance for a controlled release product). MWTA is also presented as a potential tool for real-time release for those cases where the textural features can be proven to provide accurate enough predictions of the final product performance; as shown here with the obtained prediction of dissolution from the controlled release tablet using the texture of the product as an input.

[Clinical and prognostic heterogeneity in Aicardi's syndrome: a description of two cases]

INTRODUCTION

Aicardi's syndrome is characterized by infantile spasms, agenesis of the corpus callosum and ocular lesions. Clinically it presents as severe mental retardation, severe limitation of motor development and of language, with a prognosis of survival for only a few months or years. We present two new cases of this uncommon syndrome and describe the heterogeneity of its clinical and prognostic severity.

CLINICAL CASES

Case 1. A ten-month old patient had flexion spasms of the limbs at the age of 4 months, bilateral corioretinal lesions and generalized hypoplasia of the corpus callosum. During the clinical course of the disorder, the epileptic crises were controlled, there was mental retardation, the head was held steady and the baby could sit. Case 2. A nine year old patient had had flexion spasms when aged 2 months, had bilateral retinal lesions and generalized hypoplasia of the corpus callosum. During his clinical course the epileptic crises were controlled, there was severe mental retardation, the patient could pay attention and collaborate, articulate single words, walk on his own and manipulate objects.

CONCLUSION

Aicardi's syndrome should be considered to be a syndrome in which the clinical findings and prognosis are heterogeneous, as seen from new cases with less clinical and functional limitation than the patients first described.

Molecular genetic analysis of a compound heterozygote for the glycoprotein (GP) IIb gene associated with Glanzmann's thrombasthenia: disruption of the 674-687 disulfide bridge in GPIIb prevents surface exposure of GPIIb-IIIa complexes

This work was aimed at elucidating the molecular genetic lesion(s) responsible for the thrombasthenic phenotype of a patient whose low platelet content of glycoprotein (GP) IIb-IIIa indicated that it was a case of type II Glanzmann's thrombasthenia (GT). The parents did not admit consanguinity and showed a reduced platelet content of GPIIb-IIIa. Polymerase chain reaction (PCR)-single-stranded conformational polymorphism analysis of genomic DNA showed no mutations in the patient's GPIIIa and two novel mutations in the GPIIb gene: one of them was a heterozygous splice junction mutation, a C-->A transversion, at position +2 of the exon 5-intron 5 boundary [IVS5(+2)C-->A] inherited from the father. The predicted effect of this mutation, insertion of intron 5 (76 bp) into the GPIIb-mRNA, was confirmed by reverse transcription-PCR analysis of platelet mRNA. The almost complete absence of this mutated form of GPIIb-mRNA suggests that it is very unstable. Virtually all of the proband's GPIIb-mRNA was accounted for by the allele inherited from the mother showing a T2113-->C transition that changes Cys674-->Arg674 disrupting the 674-687 intramolecular disulfide bridge. The proband showed a platelet accumulation of proGPIIb and minute amounts of GPIIb and GPIIIa. Moreover, transfection and immunoprecipitation analysis demonstrated that [Arg674]GPIIb is capable of forming a heterodimer complex with GPIIIa, but the rate of subunit maturation and the surface exposure of GPIIb-IIIa are strongly reduced. Thus, the intramolecular 674-687 disulfide bridge in GPIIb is essential for the normal processing of GPIIb-IIIa complexes. The additive effect of these two GPIIb mutations provides the molecular basis for the thrombasthenic phenotype of the proband.