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Optimizing combination therapies with existing and future CML drugs.

Katouli AA, Komarova NL - PLoS ONE (2010)

Bottom Line: Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML).Many new compounds active against T315I mutants are now at different stages of development.Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California Irvine, Irvine, California, United States of America.

ABSTRACT
Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2].

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The probability of treatment success as a function of the numbers  and .Different markers correspond to different treatment parameters: circles (division rate L = 10, death rate d = 9, drug-induced death rate hi = 10, mutation rate u = 10−7, cancerous population size at the start of treatment N = 1010), squares (), diamonds (), triangles (). Empty markers denote three-drug treatments, and solid ones – two-drug treatments. The data are presented in tables 5 and 6.
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pone-0012300-g002: The probability of treatment success as a function of the numbers and .Different markers correspond to different treatment parameters: circles (division rate L = 10, death rate d = 9, drug-induced death rate hi = 10, mutation rate u = 10−7, cancerous population size at the start of treatment N = 1010), squares (), diamonds (), triangles (). Empty markers denote three-drug treatments, and solid ones – two-drug treatments. The data are presented in tables 5 and 6.

Mentions: To demonstrate this, we calculated the probabilities of treatment success using several different parameters, and we found that an increase in or results in a decrease in the probability of treatment success, such that the numbers and give an ordering of probabilities for any tumor load. In figure 2 we present the calculated probabilities of treatment success, for tumor load of size , for different parameter values, for two-drug (solid markers) and three-drug (empty markers) treatments, as functions of the numbers and (see also tables 5 and 6).


Optimizing combination therapies with existing and future CML drugs.

Katouli AA, Komarova NL - PLoS ONE (2010)

The probability of treatment success as a function of the numbers  and .Different markers correspond to different treatment parameters: circles (division rate L = 10, death rate d = 9, drug-induced death rate hi = 10, mutation rate u = 10−7, cancerous population size at the start of treatment N = 1010), squares (), diamonds (), triangles (). Empty markers denote three-drug treatments, and solid ones – two-drug treatments. The data are presented in tables 5 and 6.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2925944&req=5

pone-0012300-g002: The probability of treatment success as a function of the numbers and .Different markers correspond to different treatment parameters: circles (division rate L = 10, death rate d = 9, drug-induced death rate hi = 10, mutation rate u = 10−7, cancerous population size at the start of treatment N = 1010), squares (), diamonds (), triangles (). Empty markers denote three-drug treatments, and solid ones – two-drug treatments. The data are presented in tables 5 and 6.
Mentions: To demonstrate this, we calculated the probabilities of treatment success using several different parameters, and we found that an increase in or results in a decrease in the probability of treatment success, such that the numbers and give an ordering of probabilities for any tumor load. In figure 2 we present the calculated probabilities of treatment success, for tumor load of size , for different parameter values, for two-drug (solid markers) and three-drug (empty markers) treatments, as functions of the numbers and (see also tables 5 and 6).

Bottom Line: Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML).Many new compounds active against T315I mutants are now at different stages of development.Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background.

View Article: PubMed Central - PubMed

Affiliation: Department of Mathematics, University of California Irvine, Irvine, California, United States of America.

ABSTRACT
Small-molecule inhibitors imatinib, dasatinib and nilotinib have been developed to treat Chromic Myeloid Leukemia (CML). The existence of a triple-cross-resistant mutation, T315I, has been a challenging problem, which can be overcome by finding new inhibitors. Many new compounds active against T315I mutants are now at different stages of development. In this paper we develop an algorithm which can weigh different combination treatment protocols according to their cross-resistance properties, and find the protocols with the highest probability of treatment success. This algorithm also takes into account drug toxicity by minimizing the number of drugs used, and their concentration. Although our methodology is based on a stochastic model of CML microevolution, the algorithm itself does not require measurements of any parameters (such as mutation rates, or division/death rates of cells), and can be used by medical professionals without a mathematical background. For illustration, we apply this algorithm to the mutation data obtained in [1], [2].

Show MeSH
Related in: MedlinePlus