Park Road, Ware, Hertfordshire
- John Bradshaw (Daylight CIS Inc., UK)
How much Chemistry does your Computer Know? [Slides]
- Andy Davis (Astra-Zeneca, UK)
Robust Assessment of Statistical Significance in Quantitative Structure-Pharmacokinetic Relationships [Slides] [Abstract]
- Tim Ebbels (Imperial College London, UK)
Pattern Recognition and NMR Spectroscopy: a Tool for Investigation of Metabolic Responses to Toxins [Slides] [Abstract]
- Marcel de Groot (Pfizer, UK)
Predicting P450 Mediated Drug Metabolism [Slides] [Abstract]
- Dave Leahy (Cyprotex, UK)
Prediction of Pharmacokinetics [Abstract]
- Richard Lloyd (GlaxoWellcome, UK)
High Throughput Aqueous Drug Solubility by Laser Nephelometry [Slides] [Abstract]
- Matthew Trotter (University College London, UK)
Support Vector Machines and their Possible Application in QSAR [Slides] [Abstract]
- Kim Watson (Oxford University, UK)
Diabetes and Computer Aided Inhibitor Design [Slides]
Presentation: John Bradshaw
How much Chemistry does your Computer Know?
Daylight CIS Inc., UK [Slides]
Presentation: Andy Davis
Robust Assessment of Statistical Significance in Quantitative Structure-Pharmacokinetic Relationships
Astra-Zeneca, UK [Slides]
The optimisation of pharmacokinetic properties remains one of the most challenging aspects of drug-design. Key parameters, clearance and volume of distribution are multi-factorial which makes deriving structure-pharmacokinetic relationships difficult. The correction of clearance and volume of distribution to their unbound parameters, as more “pure” pharmacokinetic parameters is one approach that has been taken that has enabled quantitative structure pharmacokinetic relationships to be derived. Three published datasets where unbound parameters have been correlated with lipophilicity have been re-analysed. The re-analysis has shown that high correlation coefficients can be achieved without any true correlation in the data and can lead to misinterpretation of the ways in which lipophilicity influences pharmacokinetics.
Presentation: Tim Ebbels
Pattern Recognition and NMR Spectroscopy: a Tool for Investigation of Metabolic Responses to Toxins
Imperial College London, UK [Slides]
NMR spectra of biofluids are a rich source of information on the metabolic response of organisms to toxicological or disease processes. However, the spectra are complex: they can contain thousands of overlapping signals which are highly correlated, thus making interpretation difficult. Pattern recognition (PR) methods can be used to extract this information, reducing the complexity and discerning inherent structure within the data. An important benefit of these methods is their ability to generate models which can predict the class of unknown samples. In this talk I will review various PR methods used to model biofluid NMR data and illustrate their use with brief examples from toxicology and other studies.
Presentation: Marcel de Groot
Predicting P450 Mediated Drug Metabolism
Pfizer, UK [Slides]
A combined protein and pharmacophore model for cytochrome P450 2D6 (CYP2D6) has been derived using various computational chemistry techniques and can be use to explain/predict the involvement of CYP2D6 in hydroxylation , O-demethylation and N-dealkylation reactions. For the well established CYP2D6 metabolic routes, the predictive value of the combined protein and pharmacophore model is good. P450 models, like the one presented here, have wide applications in the drug design process which will contribute to the prediction and elimination of polymorphic metabolism and drug-drug interactions.
Presentation: Dave Leahy
Prediction of Pharmacokinetics
The talk will present experimental in vitro ADME data determined for a ‘benchmark set’ of drugs for which summary human pharmacokinetic data is available. The experimental approaches will be discussed as will preliminary results from QSAR and simulation models used to evaluate the prospects for the prediction of pharmacokinetics of these compounds.
Presentation: Richard Lloyd
High Throughput Aqueous Drug Solubility by Laser Nephelometry
GlaxoWellcome, UK [Slides]
This method allows rapid qualitative and quantitative determination of drug solubility. The concentration at which a turbid suspension becomes a solution can be determined by laser nephelometry. Nephelometry can:
Detect turbidity at levels below those detectable by eye in transparent and coloured solutions.
Determine drug solubility a hundred times more quickly than by HPLC.
Access a solubility range typically from 1 to 1000mg/ml (~3 to ~500mM)
Presentation: Matthew Trotter
Support Vector Machines and their Possible Application in QSAR
University College London, UK [Slides]
A brief introduction to Support Vector Machines, a relatively recent development in the machine learning community will be given. Their potential for QSAR analysis will be outlined and, if time permits, some of the practical issues which must be considered for successful use will be discussed.
Presentation: Kim Watson
Diabetes and Computer Aided Inhibitor Design
Oxford University, UK [Slides]