How to Use a Chemotherapeutic Agent When Resistance to It Threatens the Patient

<div><p>When resistance to anticancer or antimicrobial drugs evolves in a patient, highly effective chemotherapy can fail, threatening patient health and lifespan. Standard practice is to treat aggressively, effectively eliminating drug-sensitive target cells as quickly as possible. This prevents sensitive cells from acquiring resistance de novo but also eliminates populations that can competitively suppress resistant populations. Here we analyse that evolutionary trade-off and consider recent suggestions that treatment regimens aimed at containing rather than eliminating tumours or infections might more effectively delay the emergence of resistance. Our general mathematical analysis shows that there are situations in which regimens aimed at containment will outperform standard practice even if there is no fitness cost of resistance, and, in those cases, the time to treatment failure can be more than doubled. But, there are also situations in which containment will make a bad prognosis worse. Our analysis identifies thresholds that define these situations and thus can guide treatment decisions. The analysis also suggests a variety of interventions that could be used in conjunction with cytotoxic drugs to inhibit the emergence of resistance. Fundamental principles determine, across a wide range of disease settings, the circumstances under which standard practice best delays resistance emergence—and when it can be bettered.</p></div

The impact of alternative therapies.

<p>Therapies that either decrease competitive ability (left box) or reduce the intrinsic replication rate (right box) of resistant (R) and/or sensitive (S) populations may increase (↑), decrease (↓), or leave unchanged (—) the resistance management benefits of sensitive cells. Therapies that reduce competitive ability will decrease the balance threshold, making it more likely that containment is indicated. Decreasing intrinsic replication may increase, decrease or have no effect on the balance threshold depending on whether the alternative therapy targets the sensitive cells, the resistant cells, or both. For mathematical details, see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001110#pbio.2001110.s017" target="_blank">S12 Text</a>.</p

Generic model of infection under aggressive treatment (A) and containment (B).

<p>Grey shading indicates the drug-sensitive density. Red shading indicates the drug-resistant density. Once a patient is infected, the total pathogen density (black curve) will increase until the patient experiences symptoms and seeks treatment. The infection will be treated to rapidly lower the total pathogen density to the acceptable burden (blue line). At this point (black dot), the management period begins and the time to treatment failure then depends on the subsequent treatment strategy. Under aggressive treatment (A), the total pathogen density continues to decline sharply until the infection consists only of completely drug-resistant pathogens. Under containment (B), the total pathogen density is maintained at the acceptable burden until the infection consists only of completely drug-resistant pathogens. Containment will modify the expansion of the resistant population, increasing or decreasing it (patterned areas), depending on the rate at which sensitive cells become resistant and the strength of competitive suppression.</p

Ratio of duration of management period under containment to duration of management period under aggressive treatment.

<p>The horizontal axis is the starting resistant density R(0) divided by the self-limiting density R_lim. Each colour corresponds to a different acceptable burden (blue, green, red, purple, and black correspond to acceptable burdens of 10%, 20%, 30%, 60%, and 80% of R_lim). The balance threshold is varied from 0% to 1% of R_lim. For each colour the upper curve corresponds to R_balance = 0, and the lower curve corresponds to a R_balance equal to 1% of R_lim. This range for R_balance will cover the actual expected range unless the mutation rate is quite large (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001110#pbio.2001110.s011" target="_blank">S6 Text</a> for details). Values are plotted for when the starting resistant density exceeds the balance threshold (i.e., for cases described by <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001110#pbio.2001110.g002" target="_blank">Fig 2B</a>). This figure was generated using an analytic expression for the ratio of times to treatment failure (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2001110#pbio.2001110.s011" target="_blank">S6 Text</a> for the mathematical derivation).</p

Vina-Carb: Improving Glycosidic Angles during Carbohydrate Docking

Molecular docking programs are primarily designed to align rigid, drug-like fragments into the binding sites of macromolecules and frequently display poor performance when applied to flexible carbohydrate molecules. A critical source of flexibility within an oligosaccharide is the glycosidic linkages. Recently, Carbohydrate Intrinsic (CHI) energy functions were reported that attempt to quantify the glycosidic torsion angle preferences. In the present work, the CHI-energy functions have been incorporated into the AutoDock Vina (ADV) scoring function, subsequently termed Vina-Carb (VC). Two user-adjustable parameters have been introduced, namely, a CHI- energy weight term (<i>chi_coeff</i>) that affects the magnitude of the CHI-energy penalty and a CHI-cutoff term (<i>chi_cutoff</i>) that negates CHI-energy penalties below a specified value. A data set consisting of 101 protein–carbohydrate complexes and 29 apoprotein structures was used in the development and testing of VC, including antibodies, lectins, and carbohydrate binding modules. Accounting for the intramolecular energies of the glycosidic linkages in the oligosaccharides during docking led VC to produce acceptable structures within the top five ranked poses in 74% of the systems tested, compared to a success rate of 55% for ADV. An enzyme system was employed in order to illustrate the potential application of VC to proteins that may distort glycosidic linkages of carbohydrate ligands upon binding. VC represents a significant step toward accurately predicting the structures of protein–carbohydrate complexes. Furthermore, the described approach is conceptually applicable to any class of ligands that populate well-defined conformational states

Mannobiose Binding Induces Changes in Hydrogen Bonding and Protonation States of Acidic Residues in Concanavalin A As Revealed by Neutron Crystallography

Plant lectins are carbohydrate-binding proteins with various biomedical applications. Concanavalin A (Con A) holds promise in treating cancerous tumors. To better understand the Con A carbohydrate binding specificity, we obtained a room-temperature neutron structure of this legume lectin in complex with a disaccharide Manα1–2Man, mannobiose. The neutron structure afforded direct visualization of the hydrogen bonding between the protein and ligand, showing that the ligand is able to alter both protonation states and interactions for residues located close to and distant from the binding site. An unprecedented low-barrier hydrogen bond was observed forming between the carboxylic side chains of Asp28 and Glu8, with the D atom positioned equidistant from the oxygen atoms having an O···D···O angle of 101.5°

neuraminidase trees NC/SC/OT log file

Combined log files for neuraminidase trees generated by BEAST for NC/SC/OT analysi

hemagglutinin trees NC/SC/OT log files

Combined log files for hemagglutinin trees generated by BEAST for NC/SC/OT analysi

neuraminidase trees NA/OT

Neuraminidase trees generated by BEAST for NA/OT analysis in NEXUS forma

hemagglutinin trees NC/SC/OT xml

hemagglutinin trees NC/SC/OT xm