Transfer Learning Approach to Multitarget QSRR Modeling in RPLC.

QSRR is a valuable technique for the retention time predictions of small molecules. This aims to bridge the gap between molecular structure and chromatographic behavior, offering invaluable insights for analytical chemistry. Given the challenge of simultaneous target prediction with variable experimental conditions and the scarcity of comprehensive data sets for such predictive modelings in chromatography, this study introduces a transfer learning-based multitarget QSRR approach to enhance retention time prediction. Through a comparative study of four models, both with and without the transfer learning approach, the performance of both single and multitarget QSRR was evaluated based on Mean Squared Error (MSE) and R2 metrics. Individual models were also tested for their performance against benchmark studies in this field. The findings suggest that transfer learning based multitarget models exhibit potential for enhanced accuracy in predicting retention times of small molecules, presenting a promising avenue for QSRR modeling. These models will be highly beneficial for optimizing experimental conditions in method development by better retention time predictions in Reversed-Phase Liquid Chromatography (RPLC). The reliable and effective predictive capabilities of these models make them valuable tools for pharmaceutical research and development endeavors.

Optimisation of a Microwave Synthesis of Silver Nanoparticles by a Quality by Design Approach to Improve SERS Analytical Performances.

A major limitation preventing the use of surface-enhanced Raman scattering (SERS) in routine analyses is the signal variability due to the heterogeneity of metallic nanoparticles used as SERS substrates. This study aimed to robustly optimise a synthesis process of silver nanoparticles to improve the measured SERS signal repeatability and the protocol synthesis repeatability. The process is inspired by a chemical reduction method associated with microwave irradiation to guarantee better controlled and uniform heating. The innovative Quality by Design strategy was implemented to optimise the different parameters of the process. A preliminary investigation design was firstly carried out to evaluate the influence of four parameters selected by means of an Ishikawa diagram. The critical quality attributes were to maximise the intensity of the SERS response and minimise its variance. The reaction time, temperature and stirring speed are critical process parameters. These were optimised using an I-optimal design. A robust operating zone covering the optimal reaction conditions (3.36 min-130 °C-600 rpm) associated with a probability of success was modelled. Validation of this point confirmed the prediction with intra- and inter-batch variabilities of less than 15%. In conclusion, this study successfully optimised silver nanoparticles by a rapid, low cost and simple technique enhancing the quantitative perspectives of SERS.

Quantitative Structure Retention-Relationship Modeling: Towards an Innovative General-Purpose Strategy.

Reversed-Phase Liquid Chromatography (RPLC) is a common liquid chromatographic mode used for the control of pharmaceutical compounds during their drug life cycle. Nevertheless, determining the optimal chromatographic conditions that enable this separation is time consuming and requires a lot of lab work. Quantitative Structure Retention Relationship models (QSRR) are helpful for doing this job with minimal time and cost expenditures by predicting retention times of known compounds without performing experiments. In the current work, several QSRR models were built and compared for their adequacy in predicting the retention times. The regression models were based on a combination of linear and non-linear algorithms such as Multiple Linear Regression, Support Vector Regression, Least Absolute Shrinkage and Selection Operator, Random Forest, and Gradient Boosted Regression. Models were built for five pH conditions, i.e., at pH 2.7, 3.5, 6.5, and 8.0. In the end, the model predictions were combined using stacking and the performances of all models were compared. The k-nearest neighbor-based application domain filter was established to assess the reliability of the prediction for further compound prioritization. Altogether, this study can be insightful for analytical chemists working with RPLC to begin with the computational prediction modeling such as QSRR to predict the separation of small molecules.

User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules.

In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.

Uncertainty management for In Silico screening of reversed-phase liquid chromatography methods for small compounds.

The process of developing new reversed-phase liquid chromatography methods can be both time-consuming and challenging. To meet this challenge, statistics-based strategies have emerged as cost-effective, efficient and flexible solutions. In the present study, we use a Bayesian response surface methodology, which takes advantage of the knowledge of the pKa values of the compounds present in the analyzed sample to model their retention behavior. A multi-criteria decision analysis (MCDA) was then developed to exploit the uncertainty information inherent in the model distributions. This strategic approach is designed to integrate seamlessly with quantitative structure retention relationship (QSRR) models, forming an initial in-silico screening phase. Of the two methods presented for MCDA, one showed promising results. The method development process was carried out with the optimization phase, generating a design space that corroborates the results of the selection phase.

Surface enhanced transmission Raman spectroscopy: Quantitative performances for impurity analysis in complex matrices.

A transmission detection mode was investigated with SERS analyses (SETRS). A comparison between backscattering and transmission detection modes was conducted to demonstrate the feasibility of performing SETRS analyses. The impact of various parameters on the SERS signal intensity such as sample volume, lens collection optic, laser beam size and laser power were then examined. The analytical performances of SETRS were further evaluated through the quantification of an impurity (4-aminophenol) ranging from 3 to 20 µg/mL in a commercial pharmaceutical product using a total error risk-based approach. To account for expected variability of routine analysis, 9 batches of silver nanoparticles suspensions were used and experiments were performed over 5 different days and by 2 operators. Univariate spectral analysis based on a quadratic regression was compared to a multivariate approach using a partial least square regression. The presented results demonstrated that SETRS can be used to determine an impurity in a complex matrix opening new perspectives for quantitative applications.

A multi-target QSRR approach to model retention times of small molecules in RPLC.

Quantitative structure-retention relationship models (QSRR) have been utilized as an alternative to costly and time-consuming separation analyses and associated experiments for predicting retention time. However, achieving 100 % accuracy in retention prediction is unrealistic despite the existence of various tools and approaches. The limitations of vast data availability and time complexity hinder the use of most algorithms for retention prediction. Therefore, in this study, we examined and compared two approaches for modelling retention time using a dataset of small molecules with retention times obtained at multiple conditions, referred to as multi-targets (five pH levels: 2.7, 3.5, 5, 6.5, and 8 at gradient times of 20 min of mobile phase). The first approach involved developing separate models for predicting retention time at each condition (single-target approach), while the second approach aimed to learn a single model for predicting retention across all conditions simultaneously (multi-target approach). Our findings highlight the advantages of the multi-target approach over the single-target modelling approach. The multi-target models are more efficient in terms of size and learning speed compared to the single-target models. These retention prediction models offer two-fold benefits. Firstly, they enhance knowledge and understanding of retention times, identifying molecular descriptors that contribute to changes in retention behaviour under different pH conditions. Secondly, these approaches can be extended to address other multi-target property prediction problems, such as multi-quantitative structure Property(X) relationship studies (mt-QS(X)R).

Optimization of silver nanoparticles synthesis by chemical reduction to enhance SERS quantitative performances: Early characterization using the quality by design approach.

Surface-enhanced Raman scattering (SERS) is a vibrational widely used technique thanks to its multiple advantages such as its high specificity and sensitivity. The Raman signal exaltation comes from the use of metallic nanoparticles (Nps) acting as antennas by amplifying the Raman scattering. Controlling the Nps synthesis is a major point for the implementation of SERS in routine analysis and especially in quantitative applications. Effectively, nature, size and shape of these Nps considerably influence the SERS response intensity and repeatability. The Lee-Meisel protocol is the most common synthesis route used by the SERS community due to the low cost, rapidity and ease of manufacturing. However, this process leads to a significant heterogeneity in terms of particle size and shape. In this context, this study aimed to synthesize repeatable and homogeneous silver nanoparticles (AgNps) by chemical reduction. The Quality by Design strategy from quality target product profile to early characterization design was considered to optimize this reaction. The first step of this strategy aimed to highlight critical parameters by the means of an early characterization design. Based on an Ishikawa diagram, five process parameters were studied: the reaction volume as categorical variable and the temperature, the time of reaction, the trisodium citrate concentration and pH as continuous variables. A D-Optimal design of 35 conditions was performed. Three critical quality attributes were selected to maximize the SERS intensity, minimize the variation coefficient on SERS intensities and the polydispersity index of the AgNps. Considering these factors, it appeared that concentration, pH and time of reaction were identified as having a critical impact on the Nps formation and can then be considered for the further optimization step.

Protective Effect of Ticagrelor Against Infective Endocarditis Induced by Virulent Staphylococcus aureus in Mice.

In addition to its potent antiplatelet activity, ticagrelor possesses antibacterial properties against gram-positive bacteria. We wondered whether the typical clinical dosage of ticagrelor could prevent the development of infective endocarditis caused by highly virulent Staphylococcus aureus. Ticagrelor prevented vegetation formation in a mouse model of inflammation-induced endocarditis. The dosage achieved in patients under ticagrelor therapy altered bacterial toxin production and adherence on activated endothelial cells, thereby mitigating bacterial virulence. Besides the previously described bactericidal activity at high doses, ticagrelor at typical clinical doses possesses antivirulence activity against S aureus. Ticagrelor antiplatelet activity further interferes with the interplay between platelets and bacteria.

Investigation of potential substandard dry powder inhalers on EU and North African markets - evaluation of the delivered and fine particle doses.

Purpose: Discovery of falsified Symbicort 320/9 Turbohaler identified in the UK in 2013 demonstrated that falsified dry powder inhalers were also present in the European market. This work aimed to investigate the current situation of formoterol-containing dry powder inhalers in Europe and North Africa by assessing their aerodynamic performance profile. Methods: A total of eight registered formoterol-based dry powder inhalers over the European and North African markets were involved in this study, including the reference drug Foradil. Samples were prepared using a multistage liquid impinger (MsLI) and further analyzed by a validated HPLC-UV method to determine the delivered and the fine particle doses (FPDs). This study also examined the impact of freezing-thawing cycles on sample stability in terms of analytical purpose handling. Results: No substandard dry powder inhalers were identified among the medicinal products involved in this work. The delivered dose (DD) of assessed drugs varied from 8.33 to 9.69 µg, while the FPD was between 1.86 and 3.35 µg. As expected, this work confirmed that the capsule composition and the barrier properties of the primary packaging can affect the FPD of dry powder for inhalation use. Conclusions: The FPD of products C and B was, respectively, 17.4 and 14.2% superior to Foradil, products D and H had the closest values compared to the original drug, and product F was 34.5% inferior. Additionally, this work showed that a high FPD can be achieved using HPMC capsules and moisture-impermeable primary packaging.

Generic SFC-MS methodology for the quality control of vitamin D3 oily formulations.

Vitamin D3 is a key micronutrient whose intakes are inadequate for most populations worldwide. Supplementation with medicines or food supplements is commonly prescribed to correct this imbalance and the quality of these products must be ensured. In this context, a generic methodology for the assay of vitamin D3 in oily formulations is proposed using supercritical fluid chromatography coupled to mass spectrometry (SFC-MS). It is in line with green analytical chemistry principles and combines the use of i) a fast and robust analytical method (4.0 min analysis time) ii) an easy sample preparation compatible with high throughput analysis ("dilute-and-shoot" approach) and iii) a relevant control strategy. Seventeen products from multiple manufacturers and encompassing a large content range were evaluated in this study. They were classified in four groups to streamline their processing considering the use of a matrix-matched calibration procedure. Matrix effect was thoroughly studied and was found to be low (99-106%), stable intra/inter-series and comparable between the different groups and types of matrices. The implemented control strategy was based on a three-level system suitability tests (SST). Level 1 SST: resolution of the critical pair that was above 1.5 for all analysis series. Level 2 SST: evaluation of the adequacy of the calibration for a QC sample in terms of recovery that was between 97% and 104% with a variability between 1% and 2%. Level 3 SST: method trueness that was between 95% and 102%. Sample analysis highlighted differences in types of products and dosage forms. This is the first study to propose a complete strategy for the quality control of vitamin D3 oily formulations and should prove useful in QC laboratories.

A pharmaceutical-related molecules dataset for reversed-phase chromatography retention time prediction built on combining pH and gradient time conditions.

There is a rising interest in the modeling and predicting of chromatographic retention. The progress towards more complex and comprehensive models emphasized the need for broad reliable datasets. The present dataset comprises small pharmaceutical compounds selected to cover a wide range in terms of physicochemical properties that are known to impact the retention in reversed-phase liquid chromatography. Moreover, this dataset was analyzed at five pH with two gradient slopes. It provides a reliable dataset with a diversity of conditions and compounds to support the building of new models. To enhance the robustness of the dataset, the compounds were injected individually, and each sequence of injections included a quality control sample. This unambiguous detection of each compound as well as a systematic analysis of a quality control sample ensured the quality of the reported retention times. Moreover, three different liquid chromatographic systems were used to increase the robustness of the dataset.

Analytical quality by design: Development and control strategy for a LC method to evaluate the cannabinoids content in cannabis olive oil extracts.

Cannabidiol (CBD) and Δ9-tetrahydrocannabinol (Δ9-THC) are considered as the most interesting cannabinoids in Cannabis sativa L. for the clinical practice. Since 2013, the Italian law allows pharmacists to prepare and dispense cannabis extracts to patients under medical prescription, and requires the evaluation of CBD and Δ9-THC content in cannabis extracts before sale. Cannabis olive oil extracts are prepared from dried female cannabis inflorescences, but a standard protocol is still missing. In this study, a fast RP-HPLC/UV method has been developed to quantify CBD and Δ9-THC in cannabis olive oil extracts. The analytical quality by design strategy has been applied to the method development, setting critical resolution and total analysis time as critical method attributes (CMAs), and selecting column temperature, buffer pH and flow rate as critical method parameters. Information from Doehlert Design in response surface methodology combined to Monte-Carlo simulations led to draw the risk of failure maps and to identify the method operable design region. The method was validated according to the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) guidelines and then implemented in routine analysis. A control strategy based on system control charts was planned to monitor the developed method performances. Evaluation data were recorded over a period of one year of routine use, and both the CMAs showed values within the specifications in every analysis performed. Hence, a new risk evaluation for the future performances of the method was achieved by using a Bayesian approach based on the routine use data, computing the future distribution of the two CMAs. Finally, a study focusing on the monitoring of CBD and Δ9-THC concentrations in cannabis olive oil extracts was carried out. The developed method was applied to 459 extracts. The statistical analysis of the obtained results highlighted a wide variability in terms of concentrations among different samples from the same starting typology of cannabis, underlining the compelling need of a standardised procedure to harmonise the preparation of the extracts.

Risk-based approach for method development in pharmaceutical quality control context: A critical review.

Pharmaceutical regulatory bodies increasingly require the implementation of systematic approaches in pharmaceutical product development. Quality control methods play a key role in the control strategy of drugs manufacturing to assure their quality. A risk-based approach in the analytical method development is strongly recommended to ensure that the method performances fit the purpose of the method during its entire life-cycle. In the last decade, analytical quality by design (AQbD), as risk management oriented methodology, has been progressively integrated with method development for fulfilling this objective. This approach has successfully allowed the quality to be designed into the analytical processes by obtaining a deep understanding of the procedures. In this paper the AQbD workflow and its application in the development of methods to be used for pharmaceutical quality control have been treated and discussed. Recent publications regarding how AQbD has been applied in separation techniques were reviewed. The different development strategies have been also showcased, highlighting their advantages and disadvantages, in order to give a useful overview.

Implementation of a generic SFC-MS method for the quality control of potentially counterfeited medicinal cannabis with synthetic cannabinoids.

In this study, we describe the development of a SFC-MS method for the quality control of cannabis plants that could be potentially adulterated with synthetic cannabinoids. Considering the high number of already available synthetic cannabinoids and the high rate of development of novel structures, we aimed to develop a generic method suitable for the analysis of a large panel of substances using seventeen synthetic cannabinoids from multiple classes as model compounds. Firstly, a suitable column was chosen after a screening phase. Secondly, optimal operating conditions were obtained following a robust optimization strategy based on a design of experiments and design space methodology (DoE-DS). Finally, the quantitative performances of the method were assessed with a validation according to the total error approach. The developed method has a run time of 9.4 min. It uses a simple modifier composition of methanol with 2% H2O and requires minimal sample preparation. It can chromatographically separate natural cannabinoids (except THC-A and CBD-A) from the synthetics assessed. Also, the use of mass spectrometry provides sensitivity and specificity. Moreover, this quality by design (QbD) approach permits the tuning of the method (within the DS) during routine analysis to achieve a desirable separation since the future compounds that should be analyzed could be unknown. The method was validated for the quantitation of a selected synthetic cannabinoid in fiber-type cannabis matrix over the range of 2.5% - 7.5% (w/w) with LOD value as low as 14.4 ng/mL. This generic method should be easy to implement in customs or QC laboratories in the context of counterfeit drugs tracking.

Analysis of recent pharmaceutical regulatory documents on analytical method validation.

All analysts face the same situations as method validation is the process of proving that an analytical method is acceptable for its intended purpose. In order to resolve this problem, the analyst refers to regulatory or guidance documents, and therefore the validity of the analytical methods is dependent on the guidance, terminology and methodology, proposed in these documents. It is therefore of prime importance to have clear definitions of the different validation criteria used to assess this validity. It is also necessary to have methodologies in accordance with these definitions and consequently to use statistical methods which are relevant with these definitions, the objective of the validation and the objective of the analytical method. The main purpose of this paper is to outline the inconsistencies between some definitions of the criteria and the experimental procedures proposed to evaluate those criteria in recent documents dedicated to the validation of analytical methods in the pharmaceutical field, together with the risks and problems when trying to cope with contradictory, and sometimes scientifically irrelevant, requirements and definitions.

Using tolerance intervals in pre-study validation of analytical methods to predict in-study results. The fit-for-future-purpose concept.

It is recognized that the purpose of validation of analytical methods is to demonstrate that the method is suited for its intended purpose. Validation is not only required by regulatory authorities, but is also a decisive phase before the routine use of the method. For a quantitative analytical method the objective is to quantify the target analytes with a known and suitable accuracy. For that purpose, first, a decision about the validity of the method based on prediction is proposed: a method is declared proper for routine application if it is considered that most of the future results generated will be accurate enough. This can be achieved by using the "beta-expectation tolerance interval" (accuracy profile) as the decision tool to assess the validity of the analytical method. Moreover, the concept of "fit-for-purpose" is also proposed here to select the most relevant response function as calibration curve, i.e. choosing a response function based solely on the predicted results this model will allow to obtain. This paper reports four case studies where the results obtained with quality control samples in routine were compared to predictions made in the validation phase. Predictions made using the "beta-expectation tolerance interval" are shown to be accurate and trustful for decision making. It is therefore suggested that an adequate way to conciliate both the objectives of the analytical method in routine analysis and those of the validation step consists in taking the decision about the validity of the analytical method based on prediction of the future results using the most appropriate response function curve, i.e. the fit-for-future-purpose concept.