Eric Deconinck

Benchmarking of QSAR models for blood-brain barrier permeation.

Using the largest available database of 328 blood-brain distribution (logBB) values, a quantitative benchmark was proposed to allow for a consistent comparison of the predictive accuracy of current and future logBB/quantitative structure-activity relationship (-QSAR) models. The usefulness of the benchmark was illustrated by comparing the global and k-nearest neighbors (kNN) multiple-linear regression (MLR) models based on the linear free-energy relationship (LFER) descriptors, and one non-LFER-based MLR model. The leave-one-out (LOO) and leave-group-out Monte Carlo (MC) cross-validation results (q(2) = 0.766, qms = 0.290, and qms(mc) = 0.311) indicated that the LFER-based kNN-MLR model was currently one of the most accurate predictive logBB-QSAR models. The LOO, MC, and kNN-MLR methods have been implemented in the QSAR-BENCH program, which is freely available from www.dmitrykonovalov.org for academic use.

Classification tree models for the prediction of blood−brain barrier passage of drugs

The use of classification trees for modeling and predicting the passage of molecules through the blood-brain barrier was evaluated. The models were built and evaluated using a data set of 147 molecules extracted from the literature. In the first step, single classification trees were built and evaluated for their predictive abilities. In the second step, attempts were made to improve the predictive abilities using a set of 150 classification trees in a boosting approach. Two boosting algorithms, discrete and real adaptive boosting, were used and compared. High-predictive classification trees were obtained for the data set used, and the models could be improved with boosting. In the context of this research, discrete adaptive boosting gives slightly better results than real adaptive boosting.

Statistical Confidence for Variable Selection in QSAR Models via Monte Carlo Cross-Validation

A new variable selection wrapper method named the Monte Carlo variable selection (MCVS) method was developed utilizing the framework of the Monte Carlo cross-validation (MCCV) approach. The MCVS method reports the variable selection results in the most conventional and common measure of statistical hypothesis testing, the P-values, thus allowing for a clear and simple statistical interpretation of the results. The MCVS method is equally applicable to the multiple-linear-regression (MLR)-based or non-MLR-based quantitative structure-activity relationship (QSAR) models. The method was applied to blood-brain barrier (BBB) permeation and human intestinal absorption (HIA) QSAR problems using MLR to demonstrate the workings of the new approach. Starting from more than 1600 molecular descriptors, only two (TPSA(NO) and ALOGP) yielded acceptably low P-values for the BBB and HIA problems, respectively. The new method has been implemented in the QSAR-BENCH v2 program, which is freely available (including its Java source code) from www.dmitrykonovalov.org for academic use.

Identification and characterization of peptide drugs in unknown pharmaceutical preparations seized by the Belgian authorities: case report on AOD9604

Counterfeit medicines, imitations, and substandard medicinesare a growing problem worldwide. The problem is situatedboth in developing countries as in industrialized regions. Inthe developing countries, the problem concerns the wholemedicine supply chain, especially essential medicines likeantibiotics,

Physical profiling and IR spectroscopy: simple and effective methods to discriminate between genuine and counterfeit samples of Viagra® and Cialis®

Counterfeit medicines are a global threat to public health. High amounts enter the European market, enforcing the need for simple techniques to help customs detect these pharmaceuticals. This study focused on physical profiling and IR spectroscopy to obtain a prime discrimination between genuine and illegal Viagra® and Cialis® medicines. Five post-tableting characteristics were explored: colour, mass, long length, short length, and thickness. Hypothesis testing showed that most illegal samples (between 60 and 100%) significantly differ from the genuine medicines, in particular for mass and long length. Classification and Regression Trees (CART) analysis resulted in a good discrimination between genuine and illegal medicines (98.93% correct classification rate for Viagra®, 99.42% for Cialis®). Moreover, CART confirmed the observation that mass and long length are the key physical characteristics which determine the observed discrimination. IR analysis was performed on tablets without blister and on tablets in intact blister. These data were analyzed using Soft Independent Modelling of Class Analogy (SIMCA) and Partial Least Squares - Discriminant Analysis (PLS-DA). Supervised techniques needed to be applied since Principal Component Analysis (PCA) was not able to generate the desired discrimination. Our study shows that a perfect discrimination between genuine and illegal medicines can be made by both SIMCA and PLS-DA without removing the tablets from the blister. This approach has the advantage of keeping the blister intact. Our study demonstrates that these user friendly techniques are reliable methods to aid customs to obtain a prime distinction between genuine and illegal samples on the spot. Copyright

Analysis of illegal peptide drugs via HILIC-DAD-MS

Biopharmaceuticals have established themselves as highly efficient medicines, and are still one of the fastest growing parts of the health-product industry. Unfortunately, the introduction of these promising new drugs went hand in hand with the creation of a black market for illegal and counterfeit biotechnology drugs. Particularly popular are the lyophilised peptides with a molecular weight of less than 5kDa. Most of them are meant for subcutaneous injection and are easily accessible via the internet. In recent years, different methods based on reversed phase liquid chromatography have been developed to detect and quantify these peptides. The emerging of more polar peptides however requires the introduction of other separation techniques. Therefore, we set out to develop and validate an analytical method based on hydrophilic interaction liquid chromatography (HILIC) to identify and quantify the most frequently encountered illegal peptides on the European market. For this objective, five different HILIC columns were selected and screened for their chromatographic performance. Among those columns, the ZIC HILIC column showed the best performance under the tested screening conditions in terms of resolution and symmetry factor for the targeted peptide set. Hence, the operational conditions were further optimised for the identification of illegal preparations via mass spectrometry (MS) and quantification via UV. Validation was performed via accuracy profiles based on the ISO 17025 guideline. The obtained validated HILIC-method allows for the detection and quantification of the most frequently encountered illegal peptides on the internet in a total run time of 35min including post gradient equilibration and online cleaning step. Combined with a previously developed RPLC-method, the ZIC HILIC system allows for the detection and quantification of a wide spectrum of illicit peptide drugs available on the internet. Furthermore, the developed method could also be envisaged for the detection of new emerging polar peptide drugs.

Identification of the small research tetra peptide Epitalon, assumed to be a potential treatment for cancer, old age and Retinitis Pigmentosa in two illegal pharmaceutical preparations.

SFFC (Spurious/falsely-labelled/falsified/counterfeit) medicines are a growing problem worldwide. While the problem in the developing countries concerns principally essential medicines like antibiotics, HIV medication, and anti-malaria products, the problems in the industrialized world concerns mostly drugs for erectile dysfunction, anabolic hormones, and slimming products. Although occasionally counterfeited antibiotics, insulin, and interferon are also intercepted by the customs or controlling agencies. Moreover, there is also a growing market for non-approved drugs, including peptides and proteins, which are being made available to the public before entering or completing clinical trials. Global actions are taken against criminal entities to curtail the trafficking and distribution of these substances. The growing threat of these products in Europe is mainly due to the extension of the Internet, where about 50% of the medicines sold through Internet sites, disclosing their identity, are estimated to belong to this category. In order to protect the costumer against such malice, a European directive was put in place in 2011. A new logo for online purchases has to be implemented by member states by July 2015. This logo will allow patients and consumers to identify authorized online pharmacies providing authentic, authorized medicines. At the request of the Federal Agency for Medicines and Health Products (FAMHP), unknown pharmaceutical preparations suspected to contain SFFC drugs, including protein and peptide drugs, are regularly subjected to analysis in the laboratory. Although almost all of the proteins and peptides analyzed cover potential doping agents, the SFFC version of insulin, Botulinum toxin, human interferon 1 α, nasal sprays containing the neuropeptide oxytocin, human serum albumin, and the potential toxic skin tanning peptide Melanotan II, were also found recently. In the last year, we have also received two different suspected illegal samples that contained a substance with a mass of approximately 390 Da, corresponding to a peptide with sequence AEDG. The researchers who generated this peptide, termed Epitalon, claim

A simple dilute and shoot methodology for the identification and quantification of illegal insulin

The occurrence of illegal medicines is a well-established global problem and concerns mostly small molecules. However, due to the advances in genomics and recombinant expression technologies there is an increased development of polypeptide therapeutics. Insulin is one of the best known polypeptide drug, and illegal versions of this medicine led to lethal incidents in the past. Therefore, it is crucial for the public health sector to develop reliable, efficient, cheap, unbiased and easily applicable active pharmaceutical ingredient (API) identification and quantification strategies for routine analysis of suspected illegal insulins. Here we demonstrate that our combined label-free full scan approach is not only able to distinguish between all those different versions of insulin and the insulins originating from different species, but also able to chromatographically separate human insulin and insulin lispro in conditions that are compatible with mass spectrometry (MS). Additionally, we were also able to selectively quantify the different insulins, including human insulin and insulin lispro according to the validation criteria, put forward by the United Nations (UN), for the analysis of seized illicit drugs. The proposed identification and quantification method is currently being used in our official medicines control laboratory to analyze insulins retrieved from the illegal market.

Identification of (Antioxidative) Plants in Herbal Pharmaceutical Preparations and Dietary Supplements

The standard procedures for the identification, authentication, and quality control of medicinal plants and herbs are nowadays limited to pure herbal products. No guidelines or procedures, describing the detection or identification of a targeted plant or herb in pharmaceutical preparations or dietary supplements, can be found. In these products the targeted plant is often present together with other components of herbal or synthetic origin. This chapter describes a strategy for the fast development of a chromatographic fingerprint approach that allows the identification of a targeted plant in herbal preparations and dietary supplements. The strategy consists of a standard chromatographic gradient that is tested for the targeted plant with different extraction solvents and different mobile phases. From the results obtained, the optimal fingerprint is selected. Subsequently the samples are analyzed according to the selected methodological parameters, and the obtained fingerprints can be compared with the one obtained for the pure herbal product or a standard preparation. Calculation of the dissimilarity between these fingerprints will result in a probability of presence of the targeted plant. Optionally mass spectrometry can be used to improve specificity, to confirm identification, or to identify molecules with a potential medicinal or antioxidant activity.

Orthogonal Chromatographic Descriptors for Modelling Caco-2 Drug Permeability

The use of chromatographic descriptors as alternative for Caco-2 permeability in drug absorption screening was evaluated. Therefore, retentions were measured on 17 Reversed-Phase Liquid Chromatographic systems, considered to be orthogonal or dissimilar, and an Immobilized Artificial Membrane (IAM) system. Retentions on a Micellar Liquid Chromatography system were taken from the literature. From this set of systems, those found dissimilar for the used data set were selected. The retention factors on these systems were then used as descriptors in QSAR modelling. Modelling was performed using Stepwise Multiple Linear Regression. This resulted in a model using only two chromatographic systems with good descriptive and acceptable predictive properties. A high qualitative model was obtained by combining both chromatographic systems selected in the previous model with a lipophilicity parameter (the squared Moriguchi n-octanol/water partition coefficient) and the molecular volume.