Predicting drug loading in PLA-PEG nanoparticles.

Polymer nanoparticles present advantageous physical and biopharmaceutical properties as drug delivery systems compared to conventional liquid formulations. Active pharmaceutical ingredients (APIs) are often hydrophobic, thus not soluble in conventional liquid delivery. Encapsulating the drugs in polymer nanoparticles can improve their pharmacological and bio-distribution properties, preventing rapid clearance from the bloodstream. Such nanoparticles are commonly made of non-toxic amphiphilic self-assembling block copolymers where the core (poly-[d,l-lactic acid] or PLA) serves as a reservoir for the API and the external part (Poly-(Ethylene-Glycol) or PEG) serves as a stealth corona to avoid capture by macrophage. The present study aims to predict the drug affinity for PLA-PEG nanoparticles and their effective drug loading using in silico tools in order to virtually screen potential drugs for non-covalent encapsulation applications. To that end, different simulation methods such as molecular dynamics and Monte-Carlo have been used to estimate the binding of actives on model polymer surfaces. Initially, the methods and models are validated against a series of pigments molecules for which experimental data exist. The drug affinity for the core of the nanoparticles is estimated using a Monte-Carlo "docking" method. Drug miscibility in the polymer matrix, using the Hildebrand solubility parameter (δ), and the solvation free energy of the drug in the PLA polymer model is then estimated. Finally, existing published ALogP quantitative structure-property relationships (QSPR) are compared to this method. Our results demonstrate that adsorption energies modelled by docking atomistic simulations on PLA surfaces correlate well with experimental drug loadings, whereas simpler approaches based on Hildebrand solubility parameters and Flory-Huggins interaction parameters do not. More complex molecular dynamics techniques which use estimation of the solvation free energies both in PLA and in water led to satisfactory predictive models. In addition, experimental drug loadings and Log P are found to correlate well. This work can be used to improve the understanding of drug-polymer interactions, a key component to designing better delivery systems.

Predicting drug substances autoxidation.

PURPOSE: Chemical degradation and stability in formulation is a recurrent issue in pharmaceutical development of drugs. The objective of the present study was to develop an in silico risk assessment of active pharmaceutical ingredients (APIs) stability with respect to autoxidation. METHODS: The chemical degradation by autoxidation of a diverse series of APIs has been investigated with molecular modelling tools. A set of 45 organic compounds was used to test and validate the various computational settings. Aiming to devise a methodology that could reliably perform a risk assessment for potential sensibility to autoxidation, different types of APIs, known for their autoxidation history were inspected. To define the level of approximation needed, various density functional theory (DFT) functionals and settings were employed and their accuracy and speed were compared. RESULTS: The Local Density Approximation (LDA) gave the fastest results but with a substantial deviation (systematic over-estimation) to known experimental values. The Perdew-Burke-Ernzerhof (PBE) settings appeared to be a good compromise between speed and accuracy. CONCLUSIONS: The present methodology can now be confidently deployed in pharmaceutical development for systematic risk assessment of drug stability.

A candidate gene study of antiepileptic drug tolerability and efficacy identifies an association of CYP2C9 variants with phenytoin toxicity.

BACKGROUND AND PURPOSE: It is widely acknowledged that individual response to antiepileptic drugs (AEDs) is influenced by genetic factors. However, most of the underlying genes and genetic variants remain unidentified to date. The purpose of this study is to examine the role of common variants in a number of candidate genes in the response to commonly prescribed AEDs. METHODS: We recruited 495 patients with epilepsy. Patients were classified according to their response to several AEDs. We genotyped 104 polymorphisms in 17 candidate genes for AED response. We looked for statistically significant associations between these polymorphisms and well-defined AED response phenotypes. RESULTS: We identified significant associations of CYP2C9 variant alleles with presence of phenytoin (PHT) adverse drug reactions (ADRs) and of GSTM1 copy number variation with the presence of carbamazepine ADRs. The latter association could not be confirmed in a replication study. CONCLUSIONS: Our study is the first comprehensive candidate gene association study in epilepsy pharmacogenetics. Our results confirm the role of CYP2C9 variants in PHT toxicity. No other definite associations were identified. Large-scale efforts are needed to unravel the genetic determinants of AED response.

Regulation of the alpha-fetoprotein promoter: Ku binding and DNA spatial conformation.

This work shows that the proximal promoter of the mouse Afp gene contains a Ku binding site and that Ku binding is associated with down-regulation of the transcriptional activity of the Afp promoter. The Ku binding site is located in a segment able to adopt a peculiar structured form, probably a hairpin structure. Interestingly, the structured form eliminates the binding sites of the positive transcription factor HNF1. Furthermore, a DNAse hypersensitive site is detected in footprinting experiments done with extracts of AFP non-expressing hepatoma cells. These observations suggest that the structured form is stabilised by Ku and is associated with extinction of the gene in AFP non-expressing hepatic cells.