An R Package 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.

An R Package 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

A Large Specific Deterrent Effect of Arrest for Patronizing a Prostitute

BACKGROUND: Prior research suggests that arrest, compared with no police detection, of some types of offenders does not decrease the chances they will reoffend. METHODOLOGY/PRINCIPAL FINDINGS: We assessed the specific deterrent effect of arrest for patronizing a street prostitute in Colorado Springs by comparing the incidence of arrest for clients of prostitutes first detected through public health surveillance with the incidence of rearrest for clients first detected by police arrest. Although these sets of clients were demographically and behaviorally similar, arrest reduced the likelihood of a subsequent arrest by approximately 70%. In other areas of the United States, arrest did not appear to displace a client's patronizing. CONCLUSIONS/SIGNIFICANCE: Our results suggest that apprehending clients decreases their patronizing behavior substantially

Preclinical modeling of multi-drug cancer therapies

Anticancer drugs typically are administered in the clinic in the form of mixtures, sometimes called combinations. Only in rare cases, however, are mixtures approved as drugs. Rather, research on mixtures tends to occur after single drugs have been approved. The goal of this research project was to develop modeling approaches that would encourage rational preclinical mixture design. To this end, a series of models were developed. First, several QSAR classification models were constructed to predict the cytotoxicity, oral clearance, and acute systemic toxicity of drugs. The QSAR models were applied to a set of over 115,000 natural compounds in order to identify promising ones for testing in mixtures. Second, an improved method was developed to assess synergistic, antagonistic, and additive effects between drugs in a mixture. This method, dubbed the MixLow method, is similar to the Median-Effect method, the de facto standard for assessing drug interactions. The primary difference between the two is that the MixLow method uses a nonlinear mixed-effects model to estimate parameters of concentration-effect curves, rather than an ordinary least squares procedure. Parameter estimators produced by the MixLow method were more precise than those produced by the Median-Effect Method, and coverage of Loewe index confidence intervals was superior. Third, a model was developed to predict drug interactions based on scores obtained from virtual docking experiments. This represents a novel approach for modeling drug mixtures and was more useful for the data modeled here than competing approaches. The model was applied to cytotoxicity data for 45 mixtures, each composed of up to 10 selected drugs. One drug, doxorubicin, was a standard chemotherapy agent and the others were well-known natural compounds including curcumin, EGCG, quercetin, and rhein. Predictions of synergism/antagonism were made for all possible fixed-ratio mixtures, cytotoxicities of the 10 best-scoring mixtures were tested, and drug interactions were assessed. Predicted and observed responses were highly correlated (r2 = 0.83). Results suggested that some mixtures allowed up to an 11-fold reduction of doxorubicin concentrations without sacrificing efficacy. Taken together, the models developed in this project present a general approach to rational design of mixtures during preclinical drug development

Science-Driven Societal Transformation, Part I: Worldview

Humanity faces serious social and environmental problems, including climate change and biodiversity loss. Increasingly, scientists, global policy experts, and the general public conclude that incremental approaches to reduce risk are insufficient and transformative change is needed across all sectors of society. However, the meaning of transformation is still unsettled in the literature, as is the proper role of science in fostering it. This paper is the first in a three-part series that adds to the discussion by proposing a novel science-driven research-and-development program aimed at societal transformation. More than a proposal, it offers a perspective and conceptual framework from which societal transformation might be approached. As part of this, it advances a formal mechanics with which to model and understand self-organizing societies of individuals. While acknowledging the necessity of reform to existing societal systems (e.g., governance, economic, and financial systems), the focus of the series is on transformation understood as systems change or systems migration—the de novo development of and migration to new societal systems. The series provides definitions, aims, reasoning, worldview, and a theory of change, and discusses fitness metrics and design principles for new systems. This first paper proposes a worldview, built using ideas from evolutionary biology, complex systems science, cognitive sciences, and information theory, which is intended to serve as the foundation for the R&D program. Subsequent papers in the series build on the worldview to address fitness metrics, system design, and other topics

Science-Driven Societal Transformation, Part III: Design

Climate change, biodiversity loss, and other major social and environmental problems pose severe risks. Progress has been inadequate and scientists, global policy experts, and the general public increasingly conclude that transformational change is needed across all sectors of society in order to improve and maintain social and ecological wellbeing. At least two paths to transformation are conceivable: (1) reform of and innovation within existing societal systems (e.g., economic, legal, and governance systems); and (2) the de novo development of and migration to new and improved societal systems. This paper is the final in a three-part series of concept papers that together outline a novel science-driven research and development program aimed at the second path. It summarizes literature to build a narrative on the topic of de novo design of societal systems. The purpose is to raise issues, suggest design possibilities, and highlight directions and questions that could be explored in the context of this or any R&D program aimed at new system design. This paper does not present original research, but rather provides a synthesis of selected ideas from the literature. Following other papers in the series, a society is viewed as a superorganism and its societal systems as a cognitive architecture. Accordingly, a central goal of design is to improve the collective cognitive capacity of a society, rendering it more capable of achieving and sustainably maintaining vitality. Topics of attention, communication, self-identity, power, and influence are discussed in relation to societal cognition and system design. A prototypical societal system is described, and some design considerations are highlighted

Optimality of Social Choice Systems: Complexity, Wisdom, and Wellbeing Centrality

Since circa 1900, civilization has experienced radical changes including changes in the size and distribution of populations, the power of technologies, the magnitude of energy and materials use, and the depth of scientific knowledge. With these have come increasingly complex challenges and elevated risks, and thus a heightened need for wise decision making. Accordingly, the need has grown for efficient and functional decision-making systems, also called social choice systems. I use these terms to refer to economic, governance, and legal systems. The seeming inability of societies, both individually and collectively, to effectively mitigate excessive climate change, poverty, income inequality, pollution, habitat loss, and other major problems suggests that underlying social choice systems are sub-optimal relative to need. I raise two overarching questions: (1) What characteristics would more optimal social choice systems exhibit? (2) How could research and development of more optimal systems best proceed? The answers I explore in this paper are based on the premise that the relative optimality of a social choice system is a measure of its relative capacity to help communities solve problems and organize activities such that collective wellbeing is elevated. The characteristics of complex adaptive systems, successful problem-solving systems found in nature, are explored in order to suggest useful design motifs and monitoring indicators. I emphasize the need for research and development of new social choice system designs, and argue that field testing of these can best occur at the local (e.g., community, city, or county) level. Efforts in this direction by the science and technology sectors and academic community are still nascent. The work described here suggests a new multidisciplinary program that I term wellbeing centrality: the design, testing, promotion, and operation of social choice systems that place wellbeing measurement, evaluation, forecasting, and deliberation at the center of decision-making activities