A major problem in optimally coordinating combination drug therapy is the inability to quantify and minimize the highly variable relationships between dosage of the various drugs, patient adherence, serum concentrations, drug -- drug interactions, shared therapeutic and toxic effects of the combination regimens given, and patient outcomes. Combination therapy is now the norm in many clinical settings. Our cross-disciplinary laboratory has developed parametric and especially nonparametric (NP) population modeling software to capture these relationships with statistical consistency and precision. We have also developed the new """"""""multiple model"""""""" (MM) method of dosage design to hit desired therapeutic target goals with maximum precision (minimum weighted squared error), for models of single drugs having analytic solutions to their differential equations. We have now begun clinical testing in pilot collaborative projects, to make NP population models, and to achieve target goals with maximum precision. We also have NP software to make models of the larger, nonlinear and complex interacting systems of combination therapy with multiple drugs, and their shared combination therapeutic and toxic effects.
Aim 1 : We will implement all the above in a new Windows interface.
Aim 2 : We are developing MM dosage design for the combination drug regimens. Preliminary results are most encouraging. We will develop integrated software to ensure maximally precise coordinated combination drug therapy for patients with HIV, cancer, transplants, heart failure, TB, epilepsy, those requiring combination antibiotic and antifungal therapy, and even, perhaps, diabetes mellitus. Failure to consider drugs in combination means that while each drug can be individualized, the interactions are never considered, each drug appears variable and capricious, as the changing doses of the other drugs, and their effects, are not considered, and dosage adjustment is always behind the events. Our exciting new tool should now optimize the individualization and coordination of combination drug therapy for patients, with essentially optimal Bayesian MM feedback and dosage adjustment. Subsequent feedback should tend to be more confirmatory, and dosage adjustments should be fewer and smaller. 1 can also monitor effects such as Hb, WBC, platelets, viral load, CD-4, other responses, and then make adjustments of dosage to hit all selected therapeutic targets most precisely, including tolerable degrees of toxicity (Hb=10, WBC=1200, plts=100,000 for example). All this is highly feasible and most urgently needed clinically.
Aim 3 : This work will be studied and evaluated in several collaborating pilot clinical projects, 1 on-site, others off-site. This work should greatly improve our understanding and control of combination and interacting drug relationships, and the quality and precision of combination drug therapy for patients who must receive potentially toxic drugs. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
1R01EB005803-01A1
Application #
7139743
Study Section
Special Emphasis Panel (ZRG1-SBIB-Q (50))
Program Officer
Peng, Grace
Project Start
2006-07-01
Project End
2010-04-30
Budget Start
2006-07-01
Budget End
2007-04-30
Support Year
1
Fiscal Year
2006
Total Cost
$509,830
Indirect Cost
Name
University of Southern California
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
072933393
City
Los Angeles
State
CA
Country
United States
Zip Code
90089
Jelliffe, Roger (2015) Optimal methodology is important for optimal pharmacokinetic studies, therapeutic drug monitoring and patient care. Clin Pharmacokinet 54:887-92
Jelliffe, Roger W; Schumitzky, Alan; Bayard, David et al. (2015) Describing Assay Precision-Reciprocal of Variance Is Correct, Not CV Percent: Its Use Should Significantly Improve Laboratory Performance. Ther Drug Monit 37:389-94
Jelliffe, Roger W (2014) The role of digitalis pharmacokinetics in converting atrial fibrillation and flutter to regular sinus rhythm. Clin Pharmacokinet 53:397-407
Jelliffe, Roger W; Milman, Mark; Schumitzky, Alan et al. (2014) A two-compartment population pharmacokinetic-pharmacodynamic model of digoxin in adults, with implications for dosage. Ther Drug Monit 36:387-93
Matar, Kamal M; Al-lanqawi, Yousef; Abdul-Malek, Kefaya et al. (2013) Amikacin population pharmacokinetics in critically ill Kuwaiti patients. Biomed Res Int 2013:202818
Tatarinova, Tatiana; Neely, Michael; Bartroff, Jay et al. (2013) Two general methods for population pharmacokinetic modeling: non-parametric adaptive grid and non-parametric Bayesian. J Pharmacokinet Pharmacodyn 40:189-99
Hope, William W; Vanguilder, Michael; Donnelly, J Peter et al. (2013) Software for dosage individualization of voriconazole for immunocompromised patients. Antimicrob Agents Chemother 57:1888-94
Rakhmanina, Natella Y; Neely, Michael N; Capparelli, Edmund V (2012) High dose of darunavir in treatment-experienced HIV-infected adolescent results in virologic suppression and improved CD4 cell count. Ther Drug Monit 34:237-41
Neely, Michael N; van Guilder, Michael G; Yamada, Walter M et al. (2012) Accurate detection of outliers and subpopulations with Pmetrics, a nonparametric and parametric pharmacometric modeling and simulation package for R. Ther Drug Monit 34:467-76
Jelliffe, Roger W (2012) Some comments and suggestions concerning population pharmacokinetic modeling, especially of digoxin, and its relation to clinical therapy. Ther Drug Monit 34:368-77

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