Atypical antipsychotic agents such as aripiprazole, clozapine, olanzapine, quetiapine and ziprasidone offer many advantages over conventional neuroleptics. These agents reduce negative symptoms of schizophrenia, are effective in treatment refractory cases, and have a markedly lower incidence of extrapyramidal symptoms and tardive dyskinesia. However, there is considerable patient-to-patient variability in therapeutic dose requirements of atypical antipsychotics and the propensity for side effects. Hence, the initial excitement since the introduction of atypical antipsychotics in late 1980s is now shifting towards a focus on individualization of pharmacotherapy and elucidation of the mechanistic basis of interindividual variability in drug response with use of pharmacokinetic and pharmacodynamic biomarkers. Pharmacogenomics, introduced in late 1990s, is the study of variability in drug response using information from the entire genome of a given individual patient. Both pharmacogenomics and conventional therapeutic drug monitoring (TDM) share the similar goal of improving pharmacotherapy through better explanation of individual variability in drug response. Hence, pharmacogenomic biomarkers offer a unique opportunity to complement and expand the scope of traditional TDM in clinical psychopharmacology. Importantly, pharmacogenomics enables the investigation of factors distal to drug exposure in the plasma compartment (e.g. drug targets at the biophase), thereby providing a more complete portrayal of sources of variability in psychotropic drug response. We discuss (1) the definitions for biomarkers and surrogate endpoints in the context of pharmacogenomics, (2) genetic variations in isozyme-specific atypical antipsychotic metabolism in vivo, (3) selected examples of pharmacogenomic variability in pertinent drug targets and, (4) the anticipated roadmap from implementation of pharmacogenomics to changes in healthcare and therapeutic policy. In addition, a conceptual framework that outlines the theoretical advantages of pharmacogenomics-guided TDM is presented using recent clinical applications as precedence.