Large Set Of Drugs Research Articles

The influence of water of crystallization in NIR-based MDMA·HCl detection

The large numbers of 3,4-methylenedioxy-N-methylamphetamine (MDMA) formulations encountered by the police and border security necessitates the need for safe, rapid and reliable tests to be performed on-site. Near-infrared (NIR) spectroscopy is a promising technique for on-scene illicit-drug detection because of its rapid analysis, non-invasive nature, broad scope to detect various substances, and small-sized sensors suitable for portable operation. The NIR spectrum of MDMA shows an intriguing, intense peak at ∼2000 nm that was found characteristic for MDMA within a large set of drugs and drug-related substances. Herein, we show that this peak can be attributed to water molecules of crystallization in the MDMA lattice. Drying experiments showed that both an anhydrous and hydrated form of MDMA·HCl exists with significantly different NIR spectra. At ambient conditions, the anhydrous form converted back to the hydrated form within 2 months. Our data analysis model was able to identify MDMA·HCl in mixtures of both forms. Assessment of seized casework materials showed that the majority of MDMA·HCl in The Netherlands is of the hydrated type. This is explained by the use of water-containing concentrated hydrochloric acid in the final conversion step of MDMA-base to the hydrochloride salt in clandestine laboratories. These findings provide insight in the challenges associated with NIR-based identification of drugs that may appear in various crystalline forms. Awareness on the existence of these forms and the consequences of library and data-model design to cope with this phenomenon will increase the robustness of on-site NIR-based drug detection.

Comparing mechanistic and pre-clinical predictions of volume of distribution on a large set of drugs

Comparing mechanistic and pre-clinical predictions of volume of distribution on a large set of drugs

Prediction of drug cocktail effects when the number of measurements is limited.

Cocktails of drugs can be more effective than single drugs, because they can potentially work at lower doses and avoid resistance. However, it is impossible to test all drug cocktails drawn from a large set of drugs because of the huge number of combinations. To overcome this combinatorial explosion problem, one can sample a relatively small number of combinations and use a model to predict the rest. Recently, Zimmer and Katzir et al. presented a model that accurately predicted the effects of cocktails at all doses based on measuring pairs of drugs. This model requires measuring each pair at several different doses and uses interpolation to reduce experimental noise. However, often, it is not possible to measure each pair at multiple doses (for example, in scarce patient-derived tumor material or in large screens). Here, we ask whether measurements at only a single dose can also predict high-order drug cocktails. To address this, we present a fully factorial experimental dataset on all drug cocktails built of 6 chemotherapy drugs on 2 cancer cell lines. We develop a formula that uses only pair measurements at a single dose to predict much of the variation up to 6-drug cocktails in the present data, outperforming commonly used Bliss independence and regression approaches. This model, called the pairs model, is an extension of the Bliss independence model to pairs: For M drugs, it equals the product of all pair effects to the power 1/(M−1). The pairs model also shows good agreement with previously published data on antibiotic triplets and quadruplets. The present model can only predict combinations at the same doses in which the pairs were measured and is not able to predict effects at other doses. This study indicates that pair-based approaches might be able to usefully predict and prioritize high-order combinations, even in large screens or when material for testing is limited.