Optimizing combination chemotherapy by controlling drug ratios

Cancer chemotherapy treatments typically employ drug combinations in which the dose of each agent is pushed to the brink of unacceptable toxicity; however, emerging evidence indicates that this approach may not be providing optimal efficacy due to the manner in which drugs interact. Specifically, whereas certain ratios of combined drugs can be synergistic, other ratios of the same agents may be antagonistic, implying that the most efficacious combinations may be those that utilize certain agents at reduced doses. Advances in nano-scale drug delivery vehicles now enable the translation of in vitro information on synergistic drug ratios into improved anticancer combination therapies in which the desired drug ratio can be controlled and maintained following administration in vivo, so that synergistic effects can be exploited. This "ratiometric" approach to combination chemotherapy opens new opportunities to enhance the effectiveness of existing and future treatment regimens across a spectrum of human diseases.     

Impact of drug discovery on stem cell biology

Despite the rapid technical progress in pharmaceutical industry in the past decade, it is still a great challenge to find new drugs and the situation seems more and more serious. However, the history of pharmaceutical industry clearly indicated that the significance of drug discovery went far beyond providing new drugs. For instance, drugs or candidates could be used as selective probes to reveal novel cellular mechanisms, which is a fundamental tenet of chemical biology. More interestingly, accumulating evidence indicates that drugs and candidates can find important use in stem cell biology. Not only approved drugs but also undeveloped pharmacological agents could serve as efficient agents to regulate stem cell fate. Moreover, the target and activity knowledge accumulated during the drug discovery process will help select the stem cell fate modulators in a rational manner. As the progress in stem cell biology will bring positive influence to drug discovery, it can be expected that the current drug discovery efforts will finally bear great fruits in the future.     

The clarifying role of time series data in the population genetics of HIV

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.     

Network controllability-based algorithm to target personalized driver genes for discovering combinatorial drugs of individual patients

Multiple driver genes in individual patient samples may cause resistance to individual drugs in precision medicine. However, current computational methods have not studied how to fill the gap between personalized driver gene identification and combinatorial drug discovery for individual patients. Here, we developed a novel structural network controllability-based personalized driver genes and combinatorial drug identification algorithm (CPGD), aiming to identify combinatorial drugs for an individual patient by targeting personalized driver genes from network controllability perspective. On two benchmark disease datasets (i.e. breast cancer and lung cancer datasets), performance of CPGD is superior to that of other state-of-the-art driver gene-focus methods in terms of discovery rate among prior-known clinical efficacious combinatorial drugs. Especially on breast cancer dataset, CPGD evaluated synergistic effect of pairwise drug combinations by measuring synergistic effect of their corresponding personalized driver gene modules, which are affected by a given targeting personalized driver gene set of drugs. The results showed that CPGD performs better than existing synergistic combinatorial strategies in identifying clinical efficacious paired combinatorial drugs. Furthermore, CPGD enhanced cancer subtyping by computationally providing personalized side effect signatures for individual patients. In addition, CPGD identified 90 drug combinations candidates from SARS-COV2 dataset as potential drug repurposing candidates for recently spreading COVID-19.     

In silico drug combination discovery for personalized cancer therapy

Background

Drug combination therapy, which is considered as an alternative to single drug therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug combination therapies are widely used in the clinic for hypertension, asthma, and AIDS, they have also been proposed for the treatment of cancer. However, it is difficult to select and experimentally evaluate effective combinations because not only is the number of cancer drug combinations extremely large but also the effectiveness of drug combinations varies depending on the genetic variation of cancer patients. A computational approach that prioritizes the best drug combinations considering the genetic information of a cancer patient is necessary to reduce the search space.

Results

We propose an in-silico method for personalized drug combination therapy discovery. We predict the synergy between two drugs and a cell line using genomic information, targets of drugs, and pharmacological information. We calculate and predict the synergy scores of 583 drug combinations for 31 cancer cell lines. For feature dimension reduction, we select the mutations or expression levels of the genes in cancer-related pathways. We also used various machine learning models. Extremely Randomized Trees (ERT), a tree-based ensemble model, achieved the best performance in the synergy score prediction regression task. The correlation coefficient between the synergy scores predicted by ERT and the actual observations is 0.738. To compare with an existing drug combination synergy classification model, we reformulate the problem as a binary classification problem by thresholding the synergy scores. ERT achieved an F1 score of 0.954 when synergy scores of 20 and -20 were used as the threshold, which is 8.7% higher than that obtained by the state-of-the-art baseline model. Moreover, the model correctly predicts the most synergistic combination, from approximately 100 candidate drug combinations, as the top choice for 15 out of the 31 cell lines. For 28 out of the 31 cell lines, the model predicts the most synergistic combination in the top 10 of approximately 100 candidate drug combinations. Finally, we analyze the results, generate synergistic rules using the features, and validate the rules through the literature survey.

Conclusion

Using various types of genomic information of cancer cell lines, targets of drugs, and pharmacological information, a drug combination synergy prediction pipeline is proposed. The pipeline regresses the synergy level between two drugs and a cell line as well as classifies if there exists synergy or antagonism between them. Discovering new drug combinations by our pipeline may improve personalized cancer therapy.

     

Apoptosis enzyme-linked immunosorbent assay distinguishes anticancer drugs from toxic chemicals and predicts drug synergism

The effects of anticancer drugs and toxic compounds on leukemic cells in culture were evaluated by enzyme-linked-immunosorbent assay (ELISA) based on the detection of apoptotic cells by a monoclonal antibody against single-stranded DNA. The concentrations of 13 anticancer drugs, which increased apoptosis ELISA absorbance, were similar to the concentrations decreasing long-term cell survival. Short-term metabolic tetrazolium-based 3-(4,5-dimethylthiazol-yl)-2,5-diphenyformazan bromide (MTT) assay was significantly less sensitive than apoptosis ELISA and the cell survival assay. In contrast to anticancer drugs, 12 toxic chemicals did not increase apoptosis ELISA absorbance at cytotoxic concentrations. The difference between two groups of compounds by apoptosis ELISA was especially large in cultures treated with twofold of concentrations producing 50% inhibition of cell growth: all anticancer drugs induced intense reaction (mean absorbance 2.0), while none of the toxic chemicals induced apoptosis. The application of apoptosis ELISA to chemosensitivity testing was evaluated by its ability to detect synergism of anticancer drug combinations. Among 66 drug combinations tested, only combination of nitrogen mustard with mithramycin was highly synergistic by the apoptosis ELISA, as defined by apoptosis induction with the combination containing each drug at 50% of effective concentration. This combination was also synergistic in the cell survival assay, producing significant cell kill while each drug alone had no effect on cell survival. This synergism was not detected by MTT assay. We conclude that apoptosis ELISA could be useful for drug development and chemosensitivity assessment as it can distinguish clinically useful anticancer drugs from toxic compounds, is as sensitive as the long-term cell survival assay and can detect anticancer drug synergism by rapid evaluation of apoptosis induction.     

Synergistic growth inhibition of cancer cells harboring the <Emphasis Type="Italic">RET/PTC1</Emphasis> oncogene by staurosporine and rotenone involves enhanced cell death

TPC-1 is a highly proliferative thyroid papillary carcinoma-derived cell line. These cells express the RET/PTC1 fusion protein, whose isoforms are characterized in this work. The bacterial alkaloid staurosporine and the plant extract rotenone are death-inducing drugs that have an inhibitory synergistic effect on the growth of TPC-1 cells. We show that this synergism is accompanied by an enhancement of the induction of cell death. Staurosporine alone induces cell cycle arrest in G₁, whereas rotenone induces arrest in G₂/M. We suggest that this additive pressure may drive cells to die, resulting in the synergistic interaction of the drug combination. These data emphasize the potential use of the staurosporine plus rotenone combination as an anticancer tool.     

Rapid Response to Selection,Competitive Release and Increased Transmission Potential of Artesunate-Selected Plasmodium chabaudi Malaria Parasites

The evolution of drug resistance, a key challenge for our ability to treat and control infections, depends on two processes: de-novo resistance mutations, and the selection for and spread of resistant mutants within a population. Understanding the factors influencing the rates of these two processes is essential for maximizing the useful lifespan of drugs and, therefore, effective disease control. For malaria parasites, artemisinin-based drugs are the frontline weapons in the fight against disease, but reports from the field of slower parasite clearance rates during drug treatment are generating concern that the useful lifespan of these drugs may be limited. Whether slower clearance rates represent true resistance, and how this provides a selective advantage for parasites is uncertain. Here, we show that Plasmodium chabaudi malaria parasites selected for resistance to artesunate (an artemisinin derivative) through a step-wise increase in drug dose evolved slower clearance rates extremely rapidly. In single infections, these slower clearance rates, similar to those seen in the field, provided fitness advantages to the parasite through increased overall density, recrudescence after treatment and increased transmission potential. In mixed infections, removal of susceptible parasites by drug treatment led to substantial increases in the densities and transmission potential of resistant parasites (competitive release). Our results demonstrate the double-edged sword for resistance management: in our initial selection experiments, no parasites survived aggressive chemotherapy, but after selection, the fitness advantage for resistant parasites was greatest at high drug doses. Aggressive treatment of mixed infections resulted in resistant parasites dominating the pool of gametocytes, without providing additional health benefits to hosts. Slower clearance rates can evolve rapidly and can provide a strong fitness advantage during drug treatment in both single and mixed strain infections.     

Multicomponent phytotherapeutic approach gaining momentum: Is the “one drug to fit all” model breaking down?

Natural product based drugs constitute a substantial proportion of the pharmaceutical market particularly in the therapeutic areas of infectious diseases and oncology. The primary focus of any drug development program so far has been to design selective ligands (drugs) that act on single selective disease targets to obtain highly efficacious and safe drugs with minimal side effects. Although this approach has been successful for many diseases, yet there is a significant decline in the number of new drug candidates being introduced into clinical practice over the past few decades. This serious innovation deficit that the pharmaceutical industries are facing is due primarily to the post-marketing failures of blockbuster drugs. Many analysts believe that the current capital-intensive model-“the one drug to fit all” approach will be unsustainable in future and that a new “less investment, more drugs” model is necessary for further scientific growth. It is now well established that many diseases are multi-factorial in nature and that cellular pathways operate more like webs than highways. There are often multiple ways or alternate routes that may be switched on in response to the inhibition of a specific target. This gives rise to the resistant cells or resistant organisms under the specific pressure of a targeted agent, resulting in drug resistance and clinical failure of the drug. Drugs designed to act against individual molecular targets cannot usually combat multifactorial diseases like cancer, or diseases that affect multiple tissues or cell types such as diabetes and immunoinflammatory diseases. Combination drugs that affect multiple targets simultaneously are better at controlling complex disease systems and are less prone to drug resistance. This multicomponent therapy forms the basis of phytotherapy or phytomedicine where the holistic therapeutic effect arises as a result of complex positive (synergistic) or negative (antagonistic) interactions between different components of a cocktail. In this approach, multicomponent therapy is considered to be advantageous for multifactorial diseases, instead of a “magic bullet” the metaphor of a “herbal shotgun” might better explain the state of affairs. The different interactions between various components might involve the protection of an active substance from decomposition by enzymes, modification of transport across membranes of cells or organelles, evasion of multidrug resistance mechanisms among others.     

Mathematical modeling of multi-drugs therapy: a challenge for determining the optimal combinations of antiviral drugs

In the current era of antiviral drug therapy, combining multiple drugs is a primary approach for improving antiviral effects, reducing the doses of individual drugs, relieving the side effects of strong antiviral drugs, and preventing the emergence of drug-resistant viruses. Although a variety of new drugs have been developed for HIV, HCV and influenza virus, the optimal combinations of multiple drugs are incompletely understood. To optimize the benefits of multi-drugs combinations, we must investigate the interactions between the combined drugs and their target viruses. Mathematical models of viral infection dynamics provide an ideal tool for this purpose. Additionally, whether drug combinations computed by these models are synergistic can be assessed by two prominent drug combination theories, Loewe additivity and Bliss independence. By combining the mathematical modeling of virus dynamics with drug combination theories, we could show the principles by which drug combinations yield a synergistic effect. Here, we describe the theoretical aspects of multi-drugs therapy and discuss their application to antiviral research.     

Synergistic inhibitory effects by the combination of gefitinib and genistein on NSCLC with acquired drug-resistance in vitro and in vivo

In clinical practice, most patients with non small cell lung cancer (NSCLC) who respond to tyrosine kinase inhibitors eventually progress because of an acquired resistance mutation, T790M, in epidermal growth factor receptor (EGFR). Thus, it is important to identify a new drug to reduce resistance. The aim of this study was to test whether genistein combined with gefitinib is effective against NSCLC in a cell line carrying T790M, and to clarify the underlying mechanisms. The human lung cancer cell line H1975 was used as an in vitro and in vivo model. Cells were treated with gefitinib, genistein, or a combination at a range of concentrations. Cell proliferation was calculated to assess the anticancer effects of the compounds in vitro. Flow cytometry and Western blotting were employed to determine the inhibitory effects on proliferation and the induction of apoptosis. The in vivo effects of the compounds were examined using a xenografted nude mouse model for validation. Gefitinib together with genistein enhanced both growth inhibition and apoptosis; however, the greatest synergistic effect was observed at low concentrations. p-EGFR, p-Akt, and p-mTOR expressions in vitro were reduced more by the combined use of the drugs, whereas caspase-3 and PARP activities were increased. Significantly more tumor growth inhibition was detected following combination treatment in the in vivo model. These findings suggest that genistein enhanced the antitumor effects of gefitinib in a NSCLC cell line carrying the T790M mutation. This synergistic activity may be due to increased inhibition of the downstream molecular and pro-apoptotic effects of EGFR.     

Praziquantel Synergistically Enhances Paclitaxel Efficacy to Inhibit Cancer Cell Growth

The major challenges we are facing in cancer therapy with paclitaxel (PTX) are the drug resistance and severe side effects. Massive efforts have been made to overcome these clinical challenges by combining PTX with other drugs. In this study, we reported the first preclinical data that praziquantel (PZQ), an anti-parasite agent, could greatly enhance the anticancer efficacy of PTX in various cancer cell lines, including PTX-resistant cell lines. Based on the combination index value, we demonstrated that PZQ synergistically enhanced PTX-induced cell growth inhibition. The co-treatment of PZQ and PTX also induced significant mitotic arrest and activated the apoptotic cascade. Moreover, PZQ combined with PTX resulted in a more pronounced inhibition of tumor growth compared with either drug alone in a mouse xenograft model. We tried to investigate the possible mechanisms of this synergistic efficacy induced by PZQ and PTX, and we found that the co-treatment of the two drugs could markedly decrease expression of X-linked inhibitor of apoptosis protein (XIAP), an anti-apoptotic protein. Our data further demonstrated that down-regulation of XIAP was required for the synergistic interaction between PZQ and PTX. Together, this study suggested that the combination of PZQ and PTX may represent a novel and effective anticancer strategy for optimizing PTX therapy.     

A generalized response surface model with varying relative potency for assessing drug interaction

When multiple drugs are administered simultaneously, investigators are often interested in assessing whether the drug combinations are synergistic, additive, or antagonistic. Based on the Loewe additivity reference model, many existing response surface models require constant relative potency and some of them use a single parameter to capture synergy, additivity, or antagonism. However, the assumption of constant relative potency is too restrictive, and these models using a single parameter to capture drug interaction are inadequate to describe the phenomenon when synergy, additivity, and antagonism are interspersed in different regions of drug combinations. We propose a generalized response surface model with a function of doses instead of one single parameter to identify and quantify departure from additivity. The proposed model can incorporate varying relative potencies among multiple drugs as well. Examples and simulations are given to demonstrate that the proposed model is effective in capturing different patterns of drug interaction.     

CDA: Combinatorial Drug Discovery Using Transcriptional Response Modules

Background

Anticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.

Methodology

Here we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.

Conclusions

CDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.     

Cyclin-dependent kinase inhibitors sensitize tumor cells to nutlin-induced apoptosis: a potent drug combination

Current chemotherapy focuses on the use of genotoxic drugs that may induce general DNA damage in cancer cells but also high levels of toxicity in normal tissues. Nongenotoxic activation of p53 by targeting specific molecular pathways therefore provides an attractive therapeutic strategy in cancers with wild-type p53. Here, we explored the antitumor potential of cyclin-dependent kinase (CDK) inhibitors in combination with a small molecule inhibitor of p53-murine double minute 2 (MDM2) interaction. We show that low doses of CDK inhibitors roscovitine and DRB synergize with the MDM2 antagonist nutlin-3a in the induction of p53 activity and promote p53-dependent apoptosis in a dose- and time-dependent manner. Statistical measurement of the combination effects shows that the drug combination is additive on the reduction of cell viability and synergistic on inducing apoptosis, a critical end point of cytotoxic drugs. The degree of apoptosis observed 24 to 48 h after drug treatment correlated with the accumulation of p53 protein and concomitant induction of proapoptotic proteins Puma and PIG3. The antiproliferative and cytotoxic effects of this drug combination are validated in a range of tumor-derived cells including melanoma, colon carcinoma, breast adenocarcinoma, and hepatocarcinoma cells. Furthermore, this drug combination does not induce phosphorylation of Ser(15) on p53 and does not induce genotoxic stress in the cell. Given that many cytotoxic drugs rely on their ability to induce apoptosis via DNA damage-mediated activation of p53, the data presented here may provide a new therapeutic approach for the use of CDK inhibitors and MDM2 antagonists in combinatorial drug therapy.     

Statins potently reduce the cytokine‐mediated IL‐6 release in SMC/MNC cocultures

Inflammatory pathways are involved in the development of atherosclerosis. Interaction of vessel wall cells and invading monocytes by cytokines may trigger local inflammatory processes. 3‐Hydroxy‐3‐methylglutaryl coenzyme A reductase inhibitors (statins) are standard medications used in cardiovascular diseases. They are thought to have anti‐inflammatory capacities, in addition to their lipid‐lowering effects. We investigated the anti‐inflammatory effect of statins in the cytokine‐mediated‐interaction‐model of human vascular smooth muscle cells (SMC) and human mononuclear cells (MNC). In this atherosclerosis‐related inflammatory model LPS (lipopolysaccharide, endotoxin), as well as high mobility group box 1 stimulation resulted in synergistic (i.e. over‐additive) IL‐6 (interleukin‐6) production as measured in ELISA. Recombinant IL‐1, tumour necrosis factor‐α and IL‐6 mediated the synergistic IL‐6 production. The standard anti‐inflammatory drugs aspirin and indomethacin (Indo) reduced the synergistic IL‐6 production by 60%. Simvastatin, atorvastatin, fluvastatin or pravastatin reduced the IL‐6 production by 53%, 50%, 64% and 60%, respectively. The inhibition by the statins was dose dependent. Combination of statins with aspirin and/or Indo resulted in complete inhibition of the synergistic IL‐6 production. The same inhibitors blocked STAT3 phosphorylation, providing evidence for an autocrine role of IL‐6 in the synergism. MNC from volunteers after 5 day aspirin or simvastatin administration showed no decreased IL‐6 production, probably due to drug removal during MNC isolation. Taken together, the data show that anti‐inflammatory functions (here shown for statins) can be sensitively and reproducibly determined in this novel SMC/MNC coculture model. These data implicate that statins have the capacity to affect atherosclerosis by regulating cytokine‐mediated innate inflammatory pathways in the vessel wall.     

Targeting TMPRSS2 and Cathepsin B/L together may be synergistic against SARS-CoV-2 infection

The entry of SARS-CoV-2 into target cells requires the activation of its surface spike protein, S, by host proteases. The host serine protease TMPRSS2 and cysteine proteases Cathepsin B/L can activate S, making two independent entry pathways accessible to SARS-CoV-2. Blocking the proteases prevents SARS-CoV-2 entry in vitro. This blockade may be achieved in vivo through ‘repurposing’ drugs, a potential treatment option for COVID-19 that is now in clinical trials. Here, we found, surprisingly, that drugs targeting the two pathways, although independent, could display strong synergy in blocking virus entry. We predicted this synergy first using a mathematical model of SARS-CoV-2 entry and dynamics in vitro. The model considered the two pathways explicitly, let the entry efficiency through a pathway depend on the corresponding protease expression level, which varied across cells, and let inhibitors compromise the efficiency in a dose-dependent manner. The synergy predicted was novel and arose from effects of the drugs at both the single cell and the cell population levels. Validating our predictions, available in vitro data on SARS-CoV-2 and SARS-CoV entry displayed this synergy. Further, analysing the data using our model, we estimated the relative usage of the two pathways and found it to vary widely across cell lines, suggesting that targeting both pathways in vivo may be important and synergistic given the broad tissue tropism of SARS-CoV-2. Our findings provide insights into SARS-CoV-2 entry into target cells and may help improve the deployability of drug combinations targeting host proteases required for the entry.     

Hedging against Antiviral Resistance during the Next Influenza Pandemic Using Small Stockpiles of an Alternative Chemotherapy

Background

The effectiveness of single-drug antiviral interventions to reduce morbidity and mortality during the next influenza pandemic will be substantially weakened if transmissible strains emerge which are resistant to the stockpiled antiviral drugs. We developed a mathematical model to test the hypothesis that a small stockpile of a secondary antiviral drug could be used to mitigate the adverse consequences of the emergence of resistant strains.

Methods and Findings

We used a multistrain stochastic transmission model of influenza to show that the spread of antiviral resistance can be significantly reduced by deploying a small stockpile (1% population coverage) of a secondary drug during the early phase of local epidemics. We considered two strategies for the use of the secondary stockpile: early combination chemotherapy (ECC; individuals are treated with both drugs in combination while both are available); and sequential multidrug chemotherapy (SMC; individuals are treated only with the secondary drug until it is exhausted, then treated with the primary drug). We investigated all potentially important regions of unknown parameter space and found that both ECC and SMC reduced the cumulative attack rate (AR) and the resistant attack rate (RAR) unless the probability of emergence of resistance to the primary drug p_A was so low (less than 1 in 10,000) that resistance was unlikely to be a problem or so high (more than 1 in 20) that resistance emerged as soon as primary drug monotherapy began. For example, when the basic reproductive number was 1.8 and 40% of symptomatic individuals were treated with antivirals, AR and RAR were 67% and 38% under monotherapy if p _A = 0.01. If the probability of resistance emergence for the secondary drug was also 0.01, then SMC reduced AR and RAR to 57% and 2%. The effectiveness of ECC was similar if combination chemotherapy reduced the probabilities of resistance emergence by at least ten times. We extended our model using travel data between 105 large cities to investigate the robustness of these resistance-limiting strategies at a global scale. We found that as long as populations that were the main source of resistant strains employed these strategies (SMC or ECC), then those same strategies were also effective for populations far from the source even when some intermediate populations failed to control resistance. In essence, through the existence of many wild-type epidemics, the interconnectedness of the global network dampened the international spread of resistant strains.

Conclusions

Our results indicate that the augmentation of existing stockpiles of a single anti-influenza drug with smaller stockpiles of a second drug could be an effective and inexpensive epidemiological hedge against antiviral resistance if either SMC or ECC were used. Choosing between these strategies will require additional empirical studies. Specifically, the choice will depend on the safety of combination therapy and the synergistic effect of one antiviral in suppressing the emergence of resistance to the other antiviral when both are taken in combination.     

Combining Two Methods of Global Sensitivity Analysis to Investigate MRSA Nasal Carriage Model

We apply two different sensitivity techniques to a model of bacterial colonization of the anterior nares to better understand the dynamics of Staphylococcus aureus nasal carriage. Specifically, we use partial rank correlation coefficients to investigate sensitivity as a function of time and identify a reduced model with fewer than half of the parameters of the full model. The reduced model is used for the calculation of Sobol’ indices to identify interacting parameters by their additional effects indices. Additionally, we found that the model captures an interesting characteristic of the biological phenomenon related to the initial population size of the infection; only two parameters had any significant additional effects, and these parameters have biological evidence suggesting they are connected but not yet completely understood. Sensitivity is often applied to elucidate model robustness, but we show that combining sensitivity measures can lead to synergistic insight into both model and biological structures.