Clinical trials and health care studies generate an enormous amount of data. This data is used by pharmaceutical companies during new drug development processes to characterize patient populations and determine a standardized dosage regimen for new patients, make commercial decisions, and gain approval from regulatory agencies. Nevertheless, the knowledge embedded in such data is rarely further exploited for customized patient care. In most cases, there is a significant difference between the pharmacokinetic profile of patients in a population, yet these differences are not reflected in the standardized dosage regimen. Here, a Bayesian methodology is proposed to individualize dosage regimens by combining the pharmacokinetic data collected from a patient population during clinical trials and additional data coming from a minimal number of serum samples from the new patient. In the Bayesian sense, the distribution of pharmacokinetic parameters from the population data is treated as prior information, and the posterior patient specific distribution of pharmacokinetic parameters is calculated. Then, such a posterior distribution is used to obtain dosage regimens that result in drug concentrations that are kept within the therapeutic window at a target confidence level for that patient. Moreover, a methodology is presented to suggest the sampling schedule for new patients so as to reduce the number of samples required to obtain well characterized individual pharmacometric parameters. Available pharmacokinetic data for Gabapentin, a therapeutic agent for epilepsy and neuropathic pain, is used to illustrate the concepts underlying the proposed strategy and the benefits of an individualized regimen over a standardized dosage regimen.