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PubMed2006

Nonaqueous capillary electrophoresis method for the enantiomeric purity determination of S-timolol using heptakis(2,3-di-O-methyl-6-O-sulfo)-beta-cyclodextrin: validation using the accuracy profile strategy and estimation of uncertainty.

Nonaqueous capillary electrophoresis (NACE) was successfully applied to the enantiomeric purity determination of S-timolol maleate using heptakis(2,3-di-O-methyl-6-O-sulfo)-beta-cyclodextrin (HDMS-beta-CD) as chiral selector. With a background electrolyte made up of a methanolic solution of 0.75 M formic acid, 30 mM potassium camphorsulfonate and containing 30 mM HDMS-beta-CD, the determination of 0.1% of R-timolol in S-timolol could be performed with an enantiomeric resolution of 8.5. Pyridoxine was selected as internal standard. The NACE method was then fully validated by applying a novel strategy using accuracy profiles. It is based on beta-expectation tolerance intervals for the total measurement error which includes trueness and intermediate precision. The uncertainty of measurements derived from beta-expectation tolerance intervals was estimated at each concentration level of the validation standards. To confirm the suitability of the developed and validated method, several real samples of S-timolol maleate containing R-timolol maleate at different concentrations were analysed and the results were compared to those obtained by liquid chromatography.

Antihypertensive Agents↗

PubMed2006

Interlaboratory study of a NACE method for the determination of R-timolol content in S-timolol maleate: assessment of uncertainty.

Analyses of statistical variance were applied to evaluate the precision and practicality of a CD-based NACE assay for R-timolol after enantiomeric separation of R- and S-timolol. Data were collected in an interlaboratory study by 11 participating laboratories located in Europe and North America. General qualitative method performance was examined using suitability descriptors (i.e. resolution, selectivity, migration times and S/N), while precision was determined by quantification of variances in the determination of R-timolol at four different impurity levels in S-timolol maleate samples. The interlaboratory trials were designed in accordance with the ISO guideline 5725-2. This allowed estimating for each sample, the different variances, i.e. between-laboratory (s2(Laboratories)), between-day (s2(Days)) and between-replicate (s2(Replicates)). The variances of repeatability (s2r) and reproducibility (s2R) were then calculated. The estimated uncertainty, derived from the precision estimates, seems to be concentration-dependent above a given threshold. This example of R-timolol illustrates how a laboratory can evaluate uncertainty in general.

Adrenergic beta-Antagonists↗

PubMed2004

New advances in method validation and measurement uncertainty aimed at improving the quality of chemical data.

The implementation of quality systems in analytical laboratories has now, in general, been achieved. While this requirement significantly modified the way that the laboratories were run, it has also improved the quality of the results. The key idea is to use analytical procedures which produce results that fulfil the users' needs and actually help when making decisions. This paper presents the implications of quality systems on the conception and development of an analytical procedure. It introduces the concept of the lifecycle of a method as a model that can be used to organize the selection, development, validation and routine application of a method. It underlines the importance of method validation, and presents a recent approach based on the accuracy profile to illustrate how validation must be fully integrated into the basic design of the method. Thanks to the beta-expectation tolerance interval introduced by Mee (Technometrics (1984) 26(3):251-253), it is possible to unambiguously demonstrate the fitness for purpose of a new method. Remembering that it is also a requirement for accredited laboratories to express the measurement uncertainty, the authors show that uncertainty can be easily related to the trueness and precision of the data collected when building the method accuracy profile.

Journal Article↗

PubMed2002

Towards a new age of virtual ADME/TOX and multidimensional drug discovery.

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

Biological Availability↗

PubMedDate not supplied

A fast exchange algorithm for designing focused libraries in lead optimization.

Combinatorial chemistry is widely used in drug discovery. Once a lead compound has been identified, a series of R-groups and reagents can be selected and combined to generate new potential drugs. The combinatorial nature of this problem leads to chemical libraries containing usually a very large number of virtual compounds, far too large to permit their chemical synthesis. Therefore, one often wants to select a subset of "good" reagents for each R-group of reagents and synthesize all their possible combinations. In this research, one encounters some difficulties. First, the selection of reagents has to be done such that the compounds of the resulting sublibrary simultaneously optimize a series of chemical properties. For each compound, a desirability index, a concept proposed by Harrington,(20) is used to summarize those properties in one fitness value. Then a loss function is used as objective criteria to globally quantify the quality of a sublibrary. Second, there are a huge number of possible sublibraries, and the solutions space has to be explored as fast as possible. The WEALD algorithm proposed in this paper starts with a random solution and iterates by applying exchanges, a simple method proposed by Fedorov(13) and often used in the generation of optimal designs. Those exchanges are guided by a weighting of the reagents adapted recursively as the solutions space is explored. The algorithm is applied on a real database and reveals to converge rapidly. It is compared to results given by two other algorithms presented in the combinatorial chemistry literature: the Ultrafast algorithm of D. Agrafiotis and V. Lobanov and the Piccolo algorithm of W. Zheng et al.

Algorithms↗

PubMedDate not supplied

Towards a new age of virtual ADME/TOX and multidimensional drug discovery.

With the continual pressure to ensure follow-up molecules to billion dollar blockbuster drugs, there is a hurdle in profitability and growth for pharmaceutical companies in the next decades. With each success and failure we increasingly appreciate that a key to the success of synthesized molecules through the research and development process is the possession of drug-like properties. These properties include an adequate bioactivity as well as adequate solubility, an ability to cross critical membranes (intestinal and sometimes blood-brain barrier), reasonable metabolic stability and of course safety in humans. Dependent on the therapeutic area being investigated it might also be desirable to avoid certain enzymes or transporters to circumvent potential drug-drug interactions. It may also be important to limit the induction of these same proteins that can result in further toxicities. We have clearly moved the assessment of in vitro absorption, distribution, metabolism, excretion and toxicity (ADME/TOX) parameters much earlier in the discovery organization than a decade ago with the inclusion of higher throughput systems. We are also now faced with huge amounts of ADME/TOX data for each molecule that need interpretation and also provide a valuable resource for generating predictive computational models for future drug discovery. The present review aims to show what tools exist today for visualizing and modeling ADME/TOX data, what tools need to be developed, and how both the present and future tools are valuable for virtual filtering using ADME/TOX and bioactivity properties in parallel as a viable addition to present practices.

Biological Availability↗