Han van de Waterbeemd

Lipophilicity in PK design: methyl, ethyl, futile.

Lipophilicity, often expressed as distribution coefficients (log D) in octanol/water, is an important physicochemical parameter influencing processes such as oral absorption, brain uptake and various pharmacokinetic (PK) properties. Increasing log D values increases oral absorption, plasma protein binding and volume of distribution. However, more lipophilic compounds also become more vulnerable to P450 metabolism, leading to higher clearance. Molecular size and hydrogen bonding capacity are two other properties often considered as important for membrane permeation and pharmacokinetics. Interrelationships among these physicochemical properties are discussed. Increasing size (molecular weight) often gives higher potency, but inevitably also leads to either higher lipophilicity, and hence poorer dissolution/solubility, or to more hydrogen bonding capacity, which limits oral absorption. Differences in optimal properties between gastrointestinal absorption and uptake into the brain are addressed. Special attention is given to the desired lipophilicity of CNS drugs. In examples using β-blockers, Ca channel antagonists and peptidic renin inhibitors we will demonstrate how potency and pharmacokinetic properties need to be balanced.

Pharmacokinetics and metabolism in early drug discovery.

The need for high-throughput approaches in absorption, distribution, metabolism and excretion studies is driven by the impact of high-speed chemistry and pharmacological screening. Perhaps an even greater impact is that these studies will, in the future, provide large data sets that can be used to predict biological events related to absorption, bioavailability and metabolism of drugs. Through linking of in silico and in vitro methods, considerable progress has recently been made towards this future perspective. Despite this progress, these approaches do not yet replace in vivo methods.

Predicting oral absorption and bioavailability.

Publisher Summary The prediction of oral absorption and bioavailability is an important tool, both in the early phases of drug discovery to select the most promising leads for further optimization and in the later stages to select candidates for clinical development. Progress in predicting oral absorption is based on a much better understanding of the transport processes across membranes. The identification of the key physico-chemical properties, and in addition, the identification of the key transporter proteins and metabolizing enzymes in the gut wall, has led to the development of in vitro and in vivo screens that allow reasonably accurate estimates of oral absorption in man to be made. Predicting bioavailability is more challenging. However, in recent years, very promising progress has been made using a combination of several in vitro measures, as well as the development of in silico tools.

Prediction of oral bioavailability by adaptive fuzzy partitioning

An adaptive fuzzy partition (AFP) algorithm was applied on two bioavailability data sets subdivided into four ranges of activity. A large set of molecular descriptors was tested and the most relevant parameters were selected with help of a procedure based on genetic algorithm concepts and stepwise method. After building several AFP models on a training set, the best ones were able to predict correctly 75% of the validation set compounds. Furthermore, an improvement of about 15% in the validation results was got, on the same data set, as regard to other prediction methods. The importance to work with data sets including a large molecular diversity, and to use tools able to manage it, was also shown. The prediction power was increased up to 25% employing a data set with a better-optimised molecular diversity.

Simulation models for drug disposition and drug interactions

Abstract To cope with increasing numbers of compounds and much higher demand on early ADME data, higher-throughput absorption, distribution, metabolism, and elimination (ADME) in vitro screens have been developed. However, the realisation that screening is a costly business urged pharma and biotech companies to investigate in silico prediction and simulation of pharmacokinetic data and processes. Good progress has been made in simulating the extent and rate of absorption, plasma concentration–time profiles, the site of metabolism within the compound and formed metabolites, and the effect of drug–drug interactions. Further development of robust in silico tools, in combination with high-throughput in vitro screening, will lead to an in combo approach towards drug discovery.

Hydrogen Bond Contributions to Properties and Activities of Chemicals and Drugs

Hydrogen bonding plays an important role in many chemical and biological processes, but this interaction is complex and has been difficult to quantify in correlation analysis. One of the better ways to describe hydrogen bonding strength is to use the thermodynamic parameters of H-bond formation: free energy (ΔG), enthalpy (ΔH), entropy (ΔS) and H-bond binding constant (K). These are connected to each other by the following relationships: n n

Physicochemical concepts in drug design

Delta G = - RTln K = Delta H - TDelta S

Substructure and whole molecule approaches for calculating log P.

n n(1)