Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes

Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.Comment: Since the publication of this article and among the possible improvements mentioned in the Conclusion, two improvements have been done: extending the algorithm for multivalued logic and considering the basins of attraction of the pathological attractors for selecting the therapeutic bullet

Revisiting the relationship between baseline risk and risk under treatment

<p>Abstract</p> <p>Background</p> <p>In medical practice, it is generally accepted that the 'effect model' describing the relationship between baseline risk and risk under treatment is linear, i.e. 'relative risk' is constant. Absolute benefit is then proportional to a patient's baseline risk and the treatment is most effective among high-risk patients. Alternatively, the 'effect model' becomes curvilinear when 'odds ratio' is considered to be constant. However these two models are based on purely empirical considerations, and there is still no theoretical approach to support either the linear or the non-linear relation.</p> <p>Presentation of the hypothesis</p> <p>From logistic and sigmoidal Emax (Hill) models, we derived a phenomenological model which includes the possibility of integrating both beneficial and harmful effects. Instead of a linear relation, our model suggests that the relationship is curvilinear i.e. the moderate-risk patients gain most from the treatment in opposition to those with low or high risk.</p> <p>Testing the hypothesis</p> <p>Two approaches can be proposed to investigate in practice such a model. The retrospective one is to perform a meta-analysis of clinical trials with subgroups of patients including a great range of baseline risks. The prospective one is to perform a large clinical trial in which patients are recruited according to several prestratified diverse and high risk groups.</p> <p>Implications of the hypothesis</p> <p>For the quantification of the treatment effect and considering such a model, the discrepancy between odds ratio and relative risk may be related not only to the level of risk under control conditions, but also to the characteristics of the dose-effect relation and the amount of dose administered. In the proposed approach, OR may be considered as constant in the whole range of <it>Rc</it>, and depending only on the intrinsic characteristics of the treatment. Therefore, OR should be preferred rather than RR to summarize information on treatment efficacy.</p

Enhancing Boolean networks with fuzzy operators and edge tuning

Quantitative modeling in systems biology can be difficult due to the scarcity of quantitative details about biological phenomenons, especially at the subcellular scale. An alternative to escape this difficulty is qualitative modeling since it requires few to no quantitative information. Among the qualitative modeling approaches, the Boolean network formalism is one of the most popular. However, Boolean models allow variables to be valued at only true or false, which can appear too simplistic when modeling biological processes. Consequently, this work proposes a modeling approach derived from Boolean networks where fuzzy operators are used and where edges are tuned. Fuzzy operators allow variables to be continuous and then to be more finely valued than with discrete modeling approaches, such as Boolean networks, while remaining qualitative. Moreover, to consider that in a given biological network some interactions are slower and/or weaker relative to other ones, edge states are computed in order to modulate in speed and strength the signal they convey. The proposed formalism is illustrated through its implementation on a tiny sample of the epidermal growth factor receptor signaling pathway. The obtained simulations show that continuous results are produced, thus allowing finer analysis, and that modulating the signal conveyed by the edges allows their tuning according to knowledge about the modeled interactions, thus incorporating more knowledge. The proposed modeling approach is expected to bring enhancements in the ability of qualitative models to simulate the dynamics of biological networks while not requiring quantitative information

Experience collecting interim data on mortality: an example from the RALES study

INTRODUCTION: The Randomized Aldactone Evaluation Study (RALES) randomized 822 patients to receive 25 mg spironolactone daily and 841 to receive placebo. The primary endpoint was death from all causes. Randomization began on March 24, 1995; recruitment was completed on December 31, 1996; follow-up was scheduled to continue through December 31, 1999. Evidence of a sizeable benefit on mortality emerged early in the RALES. The RALES data safety monitoring board (DSMB), which met semiannually throughout the trial, used a prespecified statistical guideline to recommend stopping for efficacy. At the DSMB's request, its meetings were preceded by an 'endpoint sweep', that is, a census of all participants to confirm their vital status. METHODS: We used computer simulation to evaluate the effect of the sweeps. RESULTS: The sweeps led to an estimated 5 to 8% increase in the number of reported deaths at the fourth and fifth interim analyses. The data crossed the statistical boundary at the fifth interim analysis. If investigators had reported all deaths within the protocol-required 24-h window, the DSMB might have recommended stopping after the fourth interim analysis. DISCUSSION: Although endpoint sweeps can cause practical problems at the clinical centers, sweeps are very useful if the intervals between patient visits or contact are long or if endpoints require adjudication by committee, reading center, or central laboratory. CONCLUSION: We recommend that trials with interim analyses institute active reporting of the primary endpoints and endpoint sweeps

The Global Risk Approach Should Be Better Applied in French Hypertensive Patients: A Comparison between Simulation and Observation Studies

The prediction of the public health impact of a preventive strategy provides valuable support for decision-making. International guidelines for hypertension management have introduced the level of absolute cardiovascular risk in the definition of the treatment target population. The public health impact of implementing such a recommendation has not been measured.We assessed the efficiency of three treatment scenarios according to historical and current versions of practice guidelines on a Realistic Virtual Population representative of the French population aged from 35 to 64 years: 1) BP≥160/95 mm Hg; 2) BP≥140/90 mm Hg and 3) BP≥140/90 mm Hg plus increased CVD risk. We compared the eligibility following the ESC guidelines with the recently observed proportion of treated amongst hypertensive individuals reported by the Etude Nationale Nutrition Santé survey. Lowering the threshold to define hypertension multiplied by 2.5 the number of eligible individuals. Applying the cardiovascular risk rule reduced this number significantly: less than 1/4 of hypertensive women under 55 years and less than 1/3 of hypertensive men below 45 years of age. This was the most efficient strategy. Compared to the simulated guidelines application, men of all ages were undertreated (between 32 and 60%), as were women over 55 years (70%). By contrast, younger women were over-treated (over 200%).The global CVD risk approach to decide for treatment is more efficient than the simple blood pressure level. However, lack of screening rather than guideline application seems to explain the low prescription rates among hypertensive individuals in France. Multidimensional analyses required to obtain these results are possible only through databases at the individual level: realistic virtual populations should become the gold standard for assessing the impact of public health policies at the national level

Reappraisal of Metformin Efficacy in the Treatment of Type 2 Diabetes: A Meta-Analysis of Randomised Controlled Trials

Catherine Cornu and colleagues performed a meta-analysis of randomised controlled trials of metformin efficacy on cardiovascular morbidity or mortality in patients with type 2 diabetes and showed that although metformin is considered the gold standard, its benefit/risk ratio remains uncertain