Chromatography

Most of the new released drug compounds are formulated as orally administered drugs due to the convenience of the oral

administration route. However, the properties of some compounds could be incompatible with oral administration. In

fact, major fi nancial losses were suff ered by pharmaceutical industry because some new drugs were discovered to have poor

bioavailability aft er their oral administration when tested in later clinical stage of development. Th erefore, drugs with poor

aqueous solubility and oral bioavailability that are considered poor candidates should be spotted as soon as possible before

reaching fi nal clinical stages of development where the costs spent on research carried out in such stages for studying the

biopharmaceutical properties of the drug is signifi cantly high, it is even better to discover these properties before the drug

is synthesized to save time and money. Over the past three decades, there has been growing interest in the prediction of the

biopharmaceutical properties as aqueous solubility and intestinal permeability of new drug entities (NDE) that resulted in

the development of a large number of experimental (in vitro and in situ) and mathematical models. In addition to being cost

eff ective and time saving, some of these models help in the determination of best drug candidates during drug discovery and

development stage. Drug intestinal permeability is one of the most important biopharmaceutical properties that are worth

investigating and predicting using the previously mentioned models. In the spectroscopic and permeation methods, we

developed mathematical models generated for prediction of human intestinal absorption (HIA) through the determination

of the micelle/water partition coeffi cients (logKxm/a) for a series of 20 compounds using UV spectroscopy and also through

determination of the permeation constants (log Kp) of a number of drugs through gels made from bile salt saturated with

infi nite dose of these drugs. Prediction models with good predictability were developed using the obtained data from both

methods along with the reference absorption data and other physicochemical properties to develop prediction equations

through simple and multiple linear regression respectively.