The public is eager to see a return on its enormous investment in the Human Genome Project. The first payoffs are anticipated in the area of pharmacogenetics, where warfarin has been called the poster child
) Warfarin is a classic test-case because it has a narrow therapeutic index, is influenced by well-characterized genetic factors, and frequently causes adverse events. Now that pharmacogenetic dosing of warfarin is commercially available and several genotyping platforms are FDA approved, a logistical barrier has become apparent: most medical centers do not have same-day VKORC1
genotyping. Even in centers that do have access, the question of how to use genotype once an INR is available remained unanswered. In fact, skeptics have argued that genetic information may be irrelevant after several days of dosing, because INR response may capture warfarin sensitivity.(24
) In part, the skeptics were right. The clinical refinement
algorithm (), which uses a single INR on day 4 or 5 of therapy, explained 26%-48% of variability in warfarin dose. For comparison, prior pharmacogenetic initiation
) explain only slightly more variability in dose (~50%). This similarity indirectly supports the claim that pharmacogenetics may add relatively little to predictive accuracy once INR data are available.
However, to compare how much genotype adds to predictive accuracy, one must compare pharmacogenetic accuracy with clinical accuracy on a particular day of therapy. We found that after 4 or 5 days of therapy, the addition of genetics improves the R2
by 12-17% (P < 0.002). These figures are averages; patients with uncommon genotypes likely benefit even more. Consider a 70-year-old patient, 5’9” (175 cm) and 200 lbs (91 kg), whose target INR is 2.5. If he presents with an INR of 1.4 after three 5-mg doses of warfarin, his predicted therapeutic dose (using the final pharmacogenetic algorithm) could be as low as 14.6 mg/week, or as high as 43.0 mg/week, depending on VKORC1-1639
genotype. For comparison, the clinical algorithm predicts 34.2 mg/week. Thus, genotype is a critical
factor determining his therapeutic dose, even when INR is monitored after 4 or 5 doses. To assist the reader in performing similar calculations, we have made the final dose-refinement and initiation algorithms freely available on www.WarfarinDosing.org
Several additional observations warrant discussion. First, VKORC1
was a more important predictor of therapeutic dose, than in our prior study of 92 orthopedic patients. Previously, we found that VKORC1
contributed modestly to dose variability once the INR after 3 doses was known.(29
) However, all of the orthopedic patients had received pharmacogenetic therapy prospectively, so the initial warfarin doses already reflected VKORC1
genotype. Second, the effect of incorporating VKORC1
into the model causes the contribution of INR to R2
to be blunted from 22.2% in the clinical model to 12.3% in the pharmacogenetic one. Thus, the pharmacogenetic model should be more robust to errors in initial INR measurements. This robustness may be helpful in patients receiving therapeutic doses of unfractionated or low-molecular-weight heparin, anticoagulants that sometimes inflate initial INR values.(33
While much of the variance can be explained by INR, prior doses, age, and (in the pharmacogenetic model) genotype, other variables also affect dose. The lower warfarin requirements in patients who have had a stroke is a new finding, and may reflect under-nutrition that is common post stroke.(34
) Our observation that diabetes is a marker for lower warfarin requirements is consistent with prior literature.(35
Several limitations also need to be discussed. As with any international collaboration, we are limited in the number of variables universally available for analysis. For example, some medications (e.g. fluconazole, rifampin, and barbiturates) interact with warfarin(36
) but were too rare to be incorporated into the model and clinicians will have to account for them (the outliers in demonstrate this necessity). The CYP4F2
V433M genotype was not collected at each site, and incorporation of this genotype might have improved the R2
) Estimated blood loss was not analyzed here, but can transiently inflate the INR after major surgery.(29
) Likewise, the algorithms do not account for decompensated heart failure or patient-specific environmental factors (e.g. dietary vitamin K intake), which may affect warfarin requirements.(38
) Finally, although the population of participants of African ancestry is relatively large (N = 123), this analysis is still based on a predominantly Caucasian population.
Pharmacogenetic predicted versus actual therapeutic doses in the internal and external validation cohorts (using an INR on day 4 of therapy)
As a reminder of the importance of considering the limitations of any particular algorithm, we look to the external validation of the final algorithm. Many of these participants (N = 139; 20%) were receiving warfarin for valve replacement. Probably because of destruction and loss of functional clotting factors during cardiopulmonary bypass and because of decreased dietary intake around valve replacement surgery, this population has a transient increased sensitivity to warfarin post-operatively.(39
) This indication, however, was rare in the internal datasets, so the algorithms had a tendency to under predict the therapeutic dosing requirements for all Inje University patients, resulting in a lower R2
and a greater MAE in the external validation cohort.
In contrast to traditional warfarin nomograms that rely on fixed initial doses and INR response alone,(42
) the refinement algorithms developed here also accommodate demographics, warfarin indication, concurrent medications, flexible prior warfarin doses, comorbidities, and genotype. The pharmacogenetic refinement algorithm had a greater R2
and lower dosing error than previous pharmacogenetic algorithms,(8
) with the exception of those tailored to specific, homogenous populations.(29
) Whether the high accuracy of the new genetic-based dosing algorithms will improve INR control or clinical outcomes is unknown, but is being addressed in three multi-centered, randomized trials in the US (Clarification of Optimal Anticoagulation through Genetics (COAG)), Genetics InFormatices Trial (GIFT) of Warfarin to Prevent DVT, and the Clinical and Economic Implications of Genetic Testing for Warfarin Management, and one in Europe (Pharmacogenetics of Anti-Coagulant Therapy (PACT)).
Personalized medicine will accomplish an important achievement if the pharmacogenetic algorithm developed here improves laboratory or clinical outcomes in ongoing trials. The hypothesized success would not belong to genetic testing, per se, but rather to a comprehensive approach whereby many patient-specific factors are accounted for explicitly. If this approach to warfarin management is any indication of what to expect from the investment in the Human Genome Project, genetics will add to, rather than replace, the list of factors that clinicians will need to consider when personalizing therapy.