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The blood-brain barrier is formed by tightly joined capillary cells with transporter proteins and acts as to regulate the brain concentration of nutrients as well as many drugs. When developing central nervous system drugs it is necessary to measure the unbound drug concentration in the brain, i.e. the unbound brain exposure. This is to ensure that the drug reaches the site of action. Furthermore, when designing new drugs it is extremely valuable to be able to predict brain exposure from a tentative drug structure. Established methods to measure total drug concentrations are of limited (if any) utility since the pharmacologically active, unbound, concentration is not obtained. The aim of the conducted research was to develop an efficient methodology to measure unbound drug in the brain and to generate a dataset for developing computational prediction models describing the relationship between drug structure and unbound brain exposure. First it was demonstrated that unbound brain exposure can be efficiently assessed using a combination of total drug concentrations in the brain and separate measurements of drug binding in the brain slices. The in vitro brain slice method was refined and made high-throughput. Improvements were also made to the in vivo measurements of total concentrations by introducing an appropriate correction for drug in residual blood. Modeling of a 43-drug dataset in the rat showed that unbound brain exposure is related to the drug hydrogen bonding potential and not to lipid solubility, which contrasts the common understanding. Further, the drug concentrations in cerebrospinal fluid approximated unbound concentrations in the brain (r2=0.80) and were also correlated with corresponding measurements in humans (r2=0.56). Therefore, rat-derived prediction models can be used when designing drugs for humans. This thesis work has provided drug industry and academia with efficient tools to obtain and to use relevant estimates of drug exposure in the brain for evaluating drugs candidates.

Several sophisticated techniques to study in vivo GI transit and regional absorption of pharmaceuticals are available and increasingly used. Examples of such methods are Magnetic Marker Monitoring (MMM) and local drug administration with remotely operated capsules. Another approach is the paracetamol and sulfapyridine double marker method which utilizes observed plasma concentrations of the two substances as markers for GI transit. Common for all of these methods is that they generate multiple types of observations e.g. tablet GI position, drug release and plasma concentrations of one or more substances. This thesis is based on the hypothesis that application of mechanistic nonlinear mixed-effect models could facilitate a better understanding of the interrelationship between such variables and result improved predictions of the processes involved in oral absorption. Mechanistic modeling approaches have been developed for application to data from MMM studies, paracetamol and sulfapyridine double marker studies and for linking in vitro and in vivo drug release. Models for integrating information about tablet GI transit, in vivo drug release and drug plasma concentrations measured in MMM studies was outlined and utilized to describe drug release and absorption properties along the GI tract for felodipine and the investigational drug AZD0837. A mechanistic link between in vitro and in vivo drug release was established by estimation of the mechanical stress in different regions of the GI tract in a unit equivalent to rotation speed in the in vitro experimental setup. The effect of atropine and erythromycin on gastric emptying and small intestinal transit was characterized with a semi-mechanistic model applied to double marker studies in fed and fasting dogs. The work with modeling of in vivo drug absorption has highlighted the need for, and led to, further development of mixed-effect modeling methodology with respect to model diagnostics and the handling of censored observations.