As is true with many other pharmacological agents, the biotransformation of practically all ADs are primarily mediated by a group of enzymes called cytochrome P-450 enzymes (CYPs) including CYP2D6, CYP2C19, CYP3A, and CYP1A2. Huge individual variations in the activities of these enzymes have long been demonstrated, much of which have been accounted for with specific allelic variations in the genes encoding these enzymes. For example, CYP2D6 allelic profiles determine whether a particular individual is a poor metabolizer (those with defective genes encoding no enzyme; approximately 2% in Han Chinese and 7% in Caucasians), intermediate metabolizer (those with “less effective” gene; approximately 50% in East Asians), extensive metabolizer (those with “wildtype” alleles; approximately 47% in East Asians) and ultrarapid metabolizer (those with gene duplication or multiplication; about 1% in East Asians and Northern Europeans, but up to 7% in Spaniards and up to 30% in Arabs and Ethiopians).¹⁰ Studies involving desipramine and venlafaxine clearly indicate that these CYP2D6 polymorphisms are mainly responsible for the pharmacokinetics, dosing, and side-effect profiles of these CYP2D6 substrates.^11,12 Similarly, specific allelic alterations also have been demonstrated to determine CYP2C19 enzyme activities, and consequently the dosing and side effect profiles of medications metabolized by this enzyme. In addition, the activity of some of these CYPs also could be significantly altered by exposure to environmental agents, whose mechanisms also have been elucidated. For example, the induction effect of St John's wort (and other natural substances) on CYP3A4 is now known to be mediated via the steroid and xenobiotic receptor fSXR], and the induction of CYP1A2 by constituents of cigarettes is mediated through the activation of the Ah receptor.¹³

Although less well documented, a number of genes other than the CYPs also influence the process of pharmacokinetics, and thus are likely to also affect the dosing and side-effect profiles of ADs. These include genes encoding transferases, such as glutathione-S-transf erase (GST) and UDP-glucurunosyltransferases (UGTs), which are responsible for drug conjugation; multldrug-resistance gene (MDR1) encoding the P-glycoprotein responsible for exporting lipophilic compounds to the extracellular space (and thus reducing drug absorption in the gut as well as inhibiting their crossing the blood-brain barrier)^14,15; and, orosomucoid 1 and 2 (ORM1 and ORM2) encoding the alpha-1-acid glycoproteins responsible for most of the often extensive binding of psychotropics to plasma proteins.^16,17 (Table I)

A number of monoamine neurotransmitter systems, including serotonin (5-HT), norepinephrine (NE), and dopamine (DA), may all play crucial roles in mediating vulnerability to depressive disorders.^18-20 Moreover, most of the commonly prescribed antidepressants are believed to exert their effects at least, in part through the modulation of either the 5-HT or the NE. systems, or both.^19,20 As the proximal site of action of many antidepressants in clinical use, the genes of the 5-HT, NE, and DA systems therefore represent attractive functional candidates in exploring antidepressant response. Each of these systems is influenced by three types of gene products: (i) those involved in biosynthesis and catabolism of the monoamines; (ii) the receptors mediating their effects; and (iii) the specific transporters which remove them from the synapses.¹⁸ Although a large number of studies have been conducted examining the association between many of these genes and antidepressant response as well as risk for mood and associated disorders, results have often been inconsistent. Of these, however, the serotonin transporter (SERT or 5-HTT) appears most, promising. As the target of SSRIs, 5-HTT clearly plays a crucial role in determining patients' response to these antidepressants, and thus it. is reasonable to speculate that functional genetic polymorphism(s) should bear clinical relevance. This indeed appears to be the case with the 5-HTT genelinked polymorphic region (5-HTTLPR), a 44 base-pair insertion/deletion in the promoter region, which significantly influences the basal transcriptional activity of 5HTT,²¹ resulting in differential 5-HTT expression and 5HT cellular uptake.²² Hariri et al²³ reported that subjects who are homozygotic for the l allele for 5-HTTLPR showed less fear and anxiety-related behaviors and exhibited less amygdala neuronal activity as assessed by functional magnetic resonance imaging in response to fearful stimuli. In congruence with this, a large number of studies have suggested association between this polymorphism and anxiety, depression and suicide risks. The relationship between 5-HTTLPR polymorphisms and antidepressant, response has been intriguing. Seven of nine studies,^24-32 including one from Taiwan²⁴, showed that the 5-HTTLPR / allele is associated with better or more rapid SSRI response. Two recent, studies also implicate the 5-HTTLPR s allele in SSRT-emergent adverse effects.^33,34

Other genes that have been the target of similar investigations include serotonin_2A receptor (5-HT2A),^35-38 dopamine transporter (DAT1),^39-46 dopamine D₂, D₃, D₄ receptor (DRD2, DRD3, DRD4), norepinephrine transporter (NET), adrenalin_2A receptor (ADRA2A),^47-50 beta adrenalin receptor (betaARs),⁵¹ Catechol-O-methyltransferase (COMT),⁵² monoamine oxidase (MAO),^53-55 tryptophan hydroxylase (TPH),^27,56,57 G-protein beta3-subunit (Gbeta3),⁵⁸ apolipoprotcin E epsilon4⁵⁹ and brain-derived neurotrophic factor (BDNF).⁶⁰ (Table II)

The remarkable advances as described above notwithstanding, the goal of achieving “individualized medicine” remains elusive. Although part, of this apparent lack of progress in the clinical application of pharmacogenomics may be attributable to existing gaps in the knowledge base, there is a general belief that the field has progressed to a point that sufficient information has already been accumulated that is clinically applicable. Factors impeding the progress in this direction have to do with infrastructure as well as data showing efficacy and cost-effectiveness of the pharmacogenomic approach.

Although for some drug-metabolizing enzymes, such as CYP2D6 and CYP2C19, allelic variations could lead to dramatic functional and health consequences, in the majority of the “candidate genes” for antidepressant response, the influence is partial and may be cumulative. This means that many genes may influence treatment response, but each with only a small effect. This is especially true with genes encoding potential therapeutic targets. Although this has been the consensus in the field for a number of years, the extant pharmacogenetic literature is predominantly based on single genotype or a combination of only a few genotypes. In order for pharmacogenetic data to be clinically useful, multiple relevant, genotypes need to be tested simultaneously, and the results need to be available for clinicians in a timely manner (preferably within 24 hours), such that the data could be included in the clinical decisions made prior to the initiation of pharmacotherapy. With the advent of high-throughput genotyping technologies, this is no longer out of reach. Thus, the next. generation of pharmacogenomic research should include the development of specific pharmacogenomic panel(s) for different disease categories and treatment methods.

Since for any disease/treatment category, such a panel will likely include a large number of “candidate genes,” whose function likely is influenced by multiple alleles, the results of the panel will be exceedingly complex and may not be easily interprétable by typical clinicians, much less readily incorporated into the clinical decision making process. To solve such a problem, a number of modeling programs have been developed. Of these, the most promising appears to be the neural network model or neural fuzzy model. Using such a model, relevant genetic data as well as clinical, sociodemographic, and lifestyle variables (past medication response history, concurrent use of other medications, dietary practices, and exposure to other drug-inducing or inhibiting agents, such as cigarette smoking) could be simultaneously incorporated into the estimations for the probability of efficacy and dosing strategy for different medications. Further, a unique feature of such a model is that it. is “trainable,” in that, as additional relevant data become available, they could be readily incorporated to improve the prediction model.