Antagonistic Drug Interactions Research Articles

Synergistic and antagonistic drug interactions are prevalent but not conserved across acute myeloid leukemia cell lines.

Acute myeloid leukemia (AML) is the most prevalent type of leukemia in adults. Its heterogeneity, both between patients and within the same patient, is often a factor contributing to poor treatment outcomes. Despite advancements in AML biology and medicine in general, the standard AML treatment, the combination of cytarabine and daunorubicin, has remained the same for decades. Combination drug therapies are proven effective in achieving targeted efficacy while minimizing drug dosage and unintended side effects, a common problem for older AML patients. However, a systematic survey of the synergistic potential of drug-drug interactions in the context of AML pathology is lacking. Here, we examine the interactions between 15 commonly used cancer drugs across distinct AML cell lines and demonstrate that synergistic and antagonistic drug-drug interactions are widespread but not conserved across these cell lines. Notably, enasidenib and venetoclax, recently approved anticancer agents, exhibited the highest counts of synergistic interactions and the fewest antagonistic ones. In contrast, 6-Thioguanine, a purine analog, was involved in the highest number of antagonistic interactions. The interactions we report here cannot be attributed solely to the inherent natures of these three drugs, as each drug we examined was involved in several synergistic or antagonistic interactions in the cell lines we tested. Importantly, these drug-drug interactions are not conserved across cell lines, suggesting that the success of combination therapies might vary significantly depending on AML genotypes. For instance, we found that a single mutation in the TF1 cell line could dramatically alter drug-drug interactions, even turning synergistic interactions into antagonistic ones. Our findings provide a preclinical survey of drug-drug interactions, revealing the complexity of the problem.

Quantifying higher-order epistasis: beware the chimera.

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Screenwerk: A modular tool for the design and analysis of drug combination screens.

There is a rapidly growing interest in high-throughput drug combination screening to identify synergizing drug interactions for treatment of various maladies, such as cancer and infectious disease. This creates the need for pipelines that can be used to design such screens, perform quality control on the data, and generate data files that can be analyzed by synergy-finding bioinformatics applications. screenwerk is an open source, end-to-end modular tool available as an R-package for the design and analysis of drug combination screens. The tool allows for a customized build of pipelines through its modularity and provides a flexible approach to quality control and data analysis. screenwerk is adaptable to various experimental requirements with an emphasis on precision medicine. It can be coupled to other R packages, such as bayesynergy, to identify synergistic and antagonistic drug interactions in cell lines or patient samples. screenwerk is scalable and provides a complete solution for setting up drug sensitivity screens, read raw measurements and consolidate different datasets, perform various types of quality control, and analyze, report and visualize the results of drug sensitivity screens. The R-package and technical documentation is available at https://github.com/Enserink-lab/screenwerk; the R source code is publicly available at https://github.com/Enserink-lab/screenwerk under GNU General Public License v3.0; bayesynergy is accessible at https://github.com/ocbe-uio/bayesynergy; Selected modules are available through Galaxy, an open-source platform for FAIR data analysis at https://oncotools.elixir.no. Supplementary data are available at Bioinformatics online.

The thiosemicarbazone, DpC, broadly synergizes with multiple anti-cancer therapeutics and demonstrates temperature- and energy-dependent uptake by tumor cells

BackgroundThe di-2-pyridylketone thiosemicarbazones, di-2-pyridylketone-4,4-dimethyl-3-thiosemicarbazone (Dp44mT) and di-2-pyridylketone-4-cyclohexyl-4-methyl-3-thiosemicarbazone (DpC), demonstrate potent and selective anti-tumor activity. In fact, DpC entered Phase I clinical trials for advanced and resistant tumors. MethodsThis investigation examined the activity of these thiosemicarbazones in five tumor cell-types compared to nine clinically used chemotherapeutics and also in combination with these drugs. ResultsDp44mT and especially DpC demonstrated potent anti-proliferative activity that was significantly greater than a range of standard anti-cancer therapeutics. As most anti-cancer drugs are given in combination, further studies were performed to examine the synergistic activity of DpC or Dp44mT with these chemotherapeutics. Combination experiments revealed broad synergy between Dp44mT or DpC upon addition of these drugs, with a sequential protocol of treating first with standard chemotherapies followed by incubation with the thiosemicarbazones being optimal. However, combining DpC and Dp44mT resulted in a pronounced antagonistic drug interaction. To dissect the mechanism of this latter effect, custom-prepared 14C-DpC was implemented and examined for its uptake by cells. The avid uptake of 14C-DpC by tumor cells observed at 37 °C was suppressed at 4 °C and by the metabolic inhibitor, sodium fluoride, suggesting a temperature- and energy-dependent mechanism. Furthermore, competition studies using an excess of unlabeled Dp44mT or DpC inhibited 14C-DpC or 14C-Dp44mT uptake, respectively, suggesting these ligands utilize the same carrier/receptor, antagonizing the internalization of each other. Conclusions and general significanceThese studies demonstrate the potent and broad anti-proliferative activity of Dp44mT and particularly DpC, and are important for establishing optimized combinations with standard chemotherapies.

The physiology and genetics of bacterial responses to antibiotic combinations.

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.

Role of synergy and antagonism in designing multidrug adaptive chemotherapy schedules.

Chemotherapeutic resistance via the mechanism of competitive release of resistant tumor cell subpopulations is a major problem associated with cancer treatments and one of the main causes of tumor recurrence. Often, chemoresistance is mitigated by using multidrug schedules (two or more combination therapies) that can act synergistically, additively, or antagonistically on the heterogeneous population of cells as they evolve. In this paper, we develop a three-component evolutionary game theory model to design two-drug adaptive schedules that mitigate chemoresistance and delay tumor recurrence in an evolving collection of tumor cells with two resistant subpopulations and one chemosensitive population that has a higher baseline fitness but is not resistant to either drug. Using the nonlinear replicator dynamical system with a payoff matrix of Prisoner's Dilemma (PD) type (enforcing a cost to resistance), we investigate the nonlinear dynamics of this three-component system along with an additional tumor growth model whose growth rate is a function of the fitness landscape of the tumor cell populations. A key parameter determines whether the two drugs interact synergistically, additively, or antagonistically. We show that antagonistic drug interactions generally result in slower rates of adaptation of the resistant cells than synergistic ones, making them more effective in combating the evolution of resistance. We then design evolutionary cycles (closed loops) in the three-component phase space by shaping the fitness landscape of the cell populations (i.e., altering the evolutionary stable states of the game) using appropriately designed time-dependent schedules (adaptive therapy), altering the dosages and timing of the two drugs. We describe two key bifurcations associated with our drug interaction parameter which help explain why antagonistic interactions are more effective at controlling competitive release of the resistant population than synergistic interactions in the context of an evolving tumor.

A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs

Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.

Combinatorial Drug Treatments Reveal Promising Anticytomegaloviral Profiles for Clinically Relevant Pharmaceutical Kinase Inhibitors (PKIs)

Human cytomegalovirus (HCMV) is a human pathogenic herpesvirus associated with a variety of clinical symptoms. Current antiviral therapy is not always effective, so that improved drug classes and drug-targeting strategies are needed. Particularly host-directed antivirals, including pharmaceutical kinase inhibitors (PKIs), may help to overcome problems of drug resistance. Here, we focused on utilizing a selection of clinically relevant PKIs and determined their anticytomegaloviral efficacies. Particularly, PKIs directed to host or viral cyclin-dependent kinases, i.e., abemaciclib, LDC4297 and maribavir, exerted promising profiles against human and murine cytomegaloviruses. The anti-HCMV in vitro activity of the approved anti-cancer drug abemaciclib was confirmed in vivo using our luciferase-based murine cytomegalovirus (MCMV) animal model in immunocompetent mice. To assess drug combinations, we applied the Bliss independence checkerboard and Loewe additivity fixed-dose assays in parallel. Results revealed that (i) both affirmative approaches provided valuable information on anti-CMV drug efficacies and interactions, (ii) the analyzed combinations comprised additive, synergistic or antagonistic drug interactions consistent with the drugs’ antiviral mode-of-action, (iii) the selected PKIs, especially LDC4297, showed promising inhibitory profiles, not only against HCMV but also other α-, β- and γ-herpesviruses, and specifically, (iv) the combination treatment with LDC4297 and maribavir revealed a strong synergism against HCMV, which might open doors towards novel clinical options in the near future. Taken together, this study highlights the potential of therapeutic drug combinations of current developmental/preclinical PKIs.

Synergistic and antagonistic drug interactions in the treatment of systemic fungal infections

Invasive fungal infections cause 1.6 million deaths annually, primarily in immunocompromised individuals. Mortality rates are as high as 90% due to limited treatments. The azole class antifungal, fluconazole, is widely available and has multi-species activity but only inhibits growth instead of killing fungal cells, necessitating long treatments. To improve treatment, we used our novel high-throughput method, the overlap2 method (O2M) to identify drugs that interact with fluconazole, either increasing or decreasing efficacy. We identified 40 molecules that act synergistically (amplify activity) and 19 molecules that act antagonistically (decrease efficacy) when combined with fluconazole. We found that critical frontline beta-lactam antibiotics antagonize fluconazole activity. A promising fluconazole-synergizing anticholinergic drug, dicyclomine, increases fungal cell permeability and inhibits nutrient intake when combined with fluconazole. In vivo, this combination doubled the time-to-endpoint of mice with Cryptococcus neoformans meningitis. Thus, our ability to rapidly identify synergistic and antagonistic drug interactions can potentially alter the patient outcomes.

Simvastatin Evokes An Unpredicted Antagonism For Tamoxifen In MCF-7 Breast Cancer Cells.

PurposeTamoxifen (TAM) is a non-steroidal antiestrogen drug, used in the prevention and treatment of all stages of hormone-responsive breast cancer. Simvastatin (SIM) is a lipid-lowering agent and has been shown to inhibit cancer cell growth. The study aimed to investigate the effect of the combination of TAM and SIM in the treatment of estrogen receptor positive (ER+) breast cancer cell line, MCF-7, and in mice-bearing Ehrlich solid tumors.MethodsMCF-7 cells were treated with different concentrations of TAM or/and SIM for 72 hours and the effects of the combination treatment on cytotoxicity, oxidative stress markers, apoptosis, angiogenesis, and metastasis were investigated using different techniques. In addition, tumor volume, oxidative markers, and inflammatory markers of the combined therapy were explored in mice bearing solid EAC tumors.ResultsThe results showed that treatment of MCF-7 cells with the combination of 10 µM TAM, and 2 µM SIM significantly inhibited the increase in oxidative stress markers, LDH, and NF-kB induced by TAM. In addition, there was a significant decrease in the total apoptotic ratio, caspase-3 activity, and glucose uptake, while there was a non-significant change in Bax/bcl-2 ratio compared to the TAM-treated group. Using the isobologram equation, the drug interaction was antagonistic with combination index, CI=1.18. On the other hand, the combination regimen decreased VEGF, and matrix metalloproteinases, MMP 2&9 compared to TAM-treated cells. Additionally, in vivo, the combination regimen resulted in a non-significant decrease in the tumor volume, decreased oxidative markers, and the protein expression of TNF-α, and NF-κB compared to the TAM treated group.ConclusionAlthough the combination regimen of TAM and SIM showed an antagonistic drug interaction in MCF-7 breast cancer, it displayed favorable antiangiogenic, anti-metastatic, and anti-inflammatory effects.

A Systematic Screen Reveals a Diverse Collection of Medications That Induce Antifungal Resistance in Candida Species.

The increasing incidence of and high mortality rates associated with invasive fungal infections (IFIs) impose an enormous clinical, social, and economic burden on humankind. In addition to microbiological resistance to existing antifungal drugs, the large number of unexplained treatment failures is a serious concern. Due to the extremely limited therapeutic options available, it is critical to identify and understand the various causes of treatment failure if patient outcomes are to improve. In this study, we examined one potential source of treatment failure: antagonistic drug interactions. Using a simple screen, we systematically identified currently approved medications that undermine the antifungal activity of three major antifungal drugs-fluconazole, caspofungin, and amphotericin B-on four prevalent human fungal pathogens-Candida albicans, Candida glabrata, Candida parapsilosis, and Candida tropicalis This revealed that a diverse collection of structurally distinct drugs exhibit antagonistic interactions with fluconazole. Several antagonistic agents selected for follow-up studies induce azole resistance through a mechanism that depends on Tac1p/Pdr1p zinc-cluster transcription factors, which activate the expression of drug efflux pumps belonging to the ABC-type transporter family. Few antagonistic interactions were identified with caspofungin or amphotericin B, possibly reflecting their cell surface mode of action that should not be affected by drug efflux mechanisms. Given that patients at greatest risk of IFIs usually receive a multitude of drugs to treat various underlying conditions, these studies suggest that chemically inducible azole resistance may be much more common and important than previously realized.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment.

Antibiotics need to be effective in diverse environments in vivo. However, the pathogen microenvironment can have a significant impact on antibiotic potency. Further, antibiotics are increasingly used in combinations to combat resistance, yet, the effect of microenvironments on drug-combination efficacy is unknown. To exhaustively explore the impact of diverse microenvironments on drug-combinations, here we develop a computational framework—Metabolism And GENomics-based Tailoring of Antibiotic regimens (MAGENTA). MAGENTA uses chemogenomic profiles of individual drugs and metabolic perturbations to predict synergistic or antagonistic drug-interactions in different microenvironments. We uncovered antibiotic combinations with robust synergy across nine distinct environments against both E. coli and A. baumannii by searching through 2556 drug-combinations of 72 drugs. MAGENTA also accurately predicted the change in efficacy of bacteriostatic and bactericidal drug-combinations during growth in glycerol media, which we confirmed experimentally in both microbes. Our approach identified genes in glycolysis and glyoxylate pathway as top predictors of synergy and antagonism respectively. Our systems approach enables tailoring of antibiotic therapies based on the pathogen microenvironment.

Antibiotic combination efficacy (ACE) networks for a Pseudomonas aeruginosa model.

The spread of antibiotic resistance is always a consequence of evolutionary processes. The consideration of evolution is thus key to the development of sustainable therapy. Two main factors were recently proposed to enhance long-term effectiveness of drug combinations: evolved collateral sensitivities between the drugs in a pair and antagonistic drug interactions. We systematically assessed these factors by performing over 1,600 evolution experiments with the opportunistic nosocomial pathogen Pseudomonas aeruginosa in single- and multidrug environments. Based on the growth dynamics during these experiments, we reconstructed antibiotic combination efficacy (ACE) networks as a new tool for characterizing the ability of the tested drug combinations to constrain bacterial survival as well as drug resistance evolution across time. Subsequent statistical analysis of the influence of the factors on ACE network characteristics revealed that (i) synergistic drug interactions increased the likelihood of bacterial population extinction—irrespective of whether combinations were compared at the same level of inhibition or not—while (ii) the potential for evolved collateral sensitivities between 2 drugs accounted for a reduction in bacterial adaptation rates. In sum, our systematic experimental analysis allowed us to pinpoint 2 complementary determinants of combination efficacy and to identify specific drug pairs with high ACE scores. Our findings can guide attempts to further improve the sustainability of antibiotic therapy by simultaneously reducing pathogen load and resistance evolution.

Novel approach for a PTX/VEGF dual drug delivery system in cardiovascular applications-an innovative bulk and surface drug immobilization.

The successive incorporation of several drugs into the polymeric bulk of implants mostly results in loss of considerable quantity of one drug, and/or the loss in quality of the coating and also in changes of drug release time points. A dual drug delivery system (DDDS) based on poly-L-lactide (PLLA) copolymers combining the effective inhibition of smooth muscle cell proliferation while simultaneously promoting re-endothelialization was successfully developed. To overcome possible antagonistic drug interactions and the limitation of the polymeric bulk material as release system for dual drugs, a novel concept which combines the bulk and surface drug immobilization for a DDDS was investigated. The advantage of this DDDS is that the bulk incorporation of fluorescein diacetate (FDAc) (model drug for paclitaxel (PTX)) via spray coating enhanced the subsequent cleavable surface coupling of vascular endothelial growth factor (VEGF) via the crosslinker bissulfosuccinimidyl suberate (BS3). In the presence of the embedded FDAc, the VEGF loading and release are about twice times higher than in absence. Furthermore, the DDDS combines the diffusion drug delivery (FDAc or PTX) and the chemical controlled drug release, VEGF via hydrolysable ester bonds, without loss in quantity and quality of the drug release curves. Additionally, the performed in vitro biocompatibility study showed the bimodal influences of PTX and VEGF on human endothelial EA.hy926 cells. In conclusion, it was possible to show the feasibility to develop a novel DDDS which has a high potential for the medical application due to the possible easy and short modification of a polymer-based PTX delivery system.

Efficient measurement and factorization of high-order drug interactions in Mycobacterium tuberculosis.

Combinations of three or more drugs are used to treat many diseases, including tuberculosis. Thus, it is important to understand how synergistic or antagonistic drug interactions affect the efficacy of combination therapies. However, our understanding of high-order drug interactions is limited because of the lack of both efficient measurement methods and theoretical framework for analysis and interpretation. We developed an efficient experimental sampling and scoring method [diagonal measurement of n-way drug interactions (DiaMOND)] to measure drug interactions for combinations of any number of drugs. DiaMOND provides an efficient alternative to checkerboard assays, which are commonly used to measure drug interactions. We established a geometric framework to factorize high-order drug interactions into lower-order components, thereby establishing a road map of how to use lower-order measurements to predict high-order interactions. Our framework is a generalized Loewe additivity model for high-order drug interactions. Using DiaMOND, we identified and analyzed synergistic and antagonistic antibiotic combinations against Mycobacteriumtuberculosis. Efficient measurement and factorization of high-order drug interactions by DiaMOND are broadly applicable to other cell types and disease models.

Abstract A102: Synthetic lethality: ABT-888 and carboplatin drug combinations are synergistic for BRCA1- and BRCA2-deficient mouse and human cells in vitro.

Abstract Background: Individuals with an inherited BRCA1 or BRCA2 (BRCA) mutation have an elevated risk of developing breast and ovarian cancer, and this BRCA defect is also present in a subset of sporadic tumors. Tumors do not have homologous recombination repair (HRR) to repair double-stranded breaks (DSBs), but they still can use other DNA repair pathways to survive. Therefore, we hypothesized that crosstalk between DNA repair pathways complements HRR in BRCA-deficient cells, leading to tumor survival. Treatment with either a poly(ADP)ribose polymerase (PARP) inhibitor (PARPi) or a platinum drug induced BRCA-deficient cell lethality by different mechanisms. In addition, combining both drugs has been reported to be synergistic in BRCA mouse models. However, the mechanism of this synergism and whether this synergism occurs in human cells are still unclear. Moreover, not all the patients respond to a PARPi chemotherapy alone. Hence, our objectives were: 1) to study the mechanism by which a PARPi combined with a platinum drug is synthetic lethal in human BRCA1 or BRCA-deficient cells; 2) to assess PARP activity as a biomarker for chemosensitivity. Methods: We compared the cytotoxic effect of ABT-888 (a PARPi) with cisplatin or carboplatin (platinum drugs) alone versus combinations by examining Brca-proficient and -deficient mouse embryonic stem (ES) cell survival using clonogenic assays. The effect of ABT-888 and carboplatin combination treatment on human cell growth was tested in HCC1937 (a triple negative BRCA1-deficient breast cancer line) and EUFA423F (a BRCA2-deficient Fanconi anemia cell line), along with their BRCA complemented lines. Synergism, antagonistic or additive drug interactions were assessed. Drug(s)-induced DSBs, HRR and apoptosis were examined and endogenous PARP activity was determined. Results: Each monotherapy killed or inhibited more Brca/BRCA-deficient cells than the corresponding Brca/BRCA-proficient cells. In Brca-deficient cells, the ABT-888 and carboplatin combination was more synergistic than ABT-888 and cisplatin. All paired BRCA-deficient or complemented cells had the same level of DNA damage following treatment with ABT-888 and/or Carboplatin. BRCA-deficient cells lack HRR, while BRCA-proficient cells use HRR. Failure to repair DSBs in BRCA-deficient cells triggered apoptosis, which was synergistic in the BRCA1-deficient cells. Moreover, higher endogenous PARP activity in Brca/BRCA-deficient cells than their respective Brca/BRCA proficient cells correlated with increased chemosensitivity. Conclusion: Our data suggest that combining ABT-888 and carboplatin will likely be more successful than monotherapy in treating most BRCA-associated cancers. In addition, high endogenous PARP activity could be used as a marker to identify PARPi susceptible tumors. Lack of HRR combined with a strong dependence on PARP activity for repair of platinum lesions led to synthetic lethality in BRCA-deficient cells. A randomized phase II trial has recently been initiated to test this drug combination in patients with BRCA-associated breast cancer and we are currently investigating γH2AX as a pharmacodynamic marker in hair follicles. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2011 Nov 12-16; San Francisco, CA. Philadelphia (PA): AACR; Mol Cancer Ther 2011;10(11 Suppl):Abstract nr A102.

AnRPackage for Assessing Drug Synergism/Antagonism

Synergistic and antagonistic drug interactions are important to consider when developing mixtures of anticancer or other types of drugs. Boik, Newman, and Boik (2008) proposed the MixLow method as an alternative to the Median-Effect method of Chou and Talalay (1984) for estimating drug interaction indices. One advantage of the MixLow method is that the nonlinear mixed-effects model used to estimate parameters of concentration-response curves can provide more accurate parameter estimates than the log linearization and least-squares analysis used in the Median-Effect method. This paper introduces the mixlow package in R, an implementation of the MixLow method. Results are reported for a small simulation study.

Assessment of Malaria In Vitro Drug Combination Screening and Mixed-Strain Infections Using the Malaria Sybr Green I-Based Fluorescence Assay

Several drug development strategies, including optimization of new antimalarial drug combinations, have been used to counter malaria drug resistance. We evaluated the malaria Sybr green I-based fluorescence (MSF) assay for its use in in vitro drug combination sensitivity assays. Drug combinations of previously published synergistic (atovaquone and proguanil), indifferent (chloroquine and azithromycin), and antagonistic (chloroquine and atovaquone) antimalarial drug interactions were tested against Plasmodium falciparum strains D6 and W2 using the MSF assay. Fifty percent inhibitory concentrations (IC(50)s) were calculated for individual drugs and in fixed ratio combinations relative to their individual IC(50)s. Subsequent isobologram analysis and fractional inhibitory concentration determinations demonstrated the expected drug interaction pattern for each combination tested. Furthermore, we explored the ability of the MSF assay to examine mixed parasite population dynamics, which are commonly seen in malaria patient isolates. Specifically, the capacity of the MSF assay to discern between single and mixed parasite populations was determined. To simulate mixed infections in vitro, fixed ratios of D6 and W2 strains were cocultured with antimalarial drugs and IC(50)s were determined using the MSF assay. Dichotomous concentration curves indicated that the sensitive and resistant parasites composing the genetically heterogeneous population were detectable. Biphasic analysis was performed to obtain subpopulation IC(50)s for comparison to those obtained for the individual malaria strains alone. In conclusion, the MSF assay allows for reliable antimalarial drug combination screening and provides an important method to discern between homogenous and heterogeneous parasite populations.

Immunosuppression with FK778 and mycophenolate mofetil in a rat cardiac transplantation model.

FK778 blocks the dihydro-orotate dehydrogenase, necessary for pyrimidine synthesis, and mycophenolate mofetil (MMF) inhibits the inosine monophosphate dehydrogenase, a crucial enzyme for purine biosynthesis. Beneficial immunosuppressive effects have been suggested for the combination of both drugs. The Brown Norway-Lewis rat heterotopic heart transplantation model was used. FK778 (5 and 20 mg/kg/day), MMF (10 and 40 mg/kg/day), or a combination of both drugs for 10 days was used for prevention of acute graft rejection. Grafts of untreated animals were rejected after 6.2 +/- 0.4 days. Low-dose FK778 and low-dose MMF administration did not result in a significantly prolonged graft survival (6.7 +/- 0.8 and 8.7 +/- 1.4 days; P=not significant). Grafts of rats treated with high-dose FK778 or high-dose MMF survived significantly longer (17.0 +/- 2.8 and 20.7 +/- 3.8 days; P<0.01). Concomitant use of low-dose FK778 with low-dose MMF produced synergistic interactions (mean survival time 12.3 +/- 2.9 days; P<0.01; combination index=0.85). High-dose drug combination (mean survival time 24.0 +/- 1.4 days) showed antagonistic drug interaction (combination index=1.55) with increased toxic side effects.

UCN-01 dose-dependent inhibition of normal hyperproliferative cells in mice

UCN-01 is a hydroxylated derivative of staurosporine and a potent protein kinase C (PKC) inhibitor. Interest in the potential usefulness of this compound as an anticancer drug stems mainly from its unique anti-signaling, growth-arresting properties on tumor cells. This include activation of CDC2 kinase (CDK1) which interacts with either cyclin A or cyclin B1 at the G1 or G2/M border, suggeting that this event is one of the major consequences of the drug action on eukaryotic cells. Nonetheless, the antiproliferative activity of UCN-01 on normal rapidly dividing cells (intestinal epithelial and bone marrow cells) is not well documented. Thus, the main objective of this study was to investigate the in vivo antiproliferative activity of UCN-01 on these normal hyperproliferative cells and evaluate whether cellular response to UCN-01 could be modulated in the presence of DNA damage. Mice were injected i.m. with a single dose of UCN-01 (2.5 mg/kg-20 mg/kg) followed 3 and 24 h later by in vivo BrdU labeling for 1 h. At autopsy, bone marrow cells were collected and fixed for dual parameter BrdU/DNA flow cytometry. Different regions of the gut were also fixed for immunoperoxidase BrdU assays. Newly replicated cells were mainly located in the lower compartments of the crypt columns and were scored for BrdU stained nuclei using an image analysis system. A comparison between groups showed that 5 mg/kg UCN-01 induced inhibition in BrdU incorporation at 3 and 24 h, as compared to the other groups injected with various doses of UCN-01. Flow cytometric analysis of bone marrow cells stained with fluorescein tagged anti-BrdU (FITC) along with propidium iodide (PI) also showed inhibition in BrdU incorporation of S phase fraction cells in mice treated with 5 mg/kg UCN-01. These bone marrow cells were arrested primarily in the G1 phase of the cell cycle. The colony-forming unit (CFU) assay of the bone marrow cells was then used to determine the level of drug interaction of UCN-01 and, topotecan, a topoisomerase I inhibitor, at a fixed dose ratio. An antagonistic drug interaction (CI > 1) was observed as determined by the median-effect analysis. However, an additive interaction (CI = 1) was obtained with the use of camptothecin or 10,11-methylenedioxycamptothecin and UCN-01. The results of the in vitro drug interaction with UCN-01 may predict protection from topotecan-induced bone marrow toxicity.