PREDICT: a method for inferring novel drug indications with application to personalized medicine

The authors present a new method, PREDICT, for the large-scale prediction of drug indications, and demonstrate its use on both approved drugs and novel molecules. They also provide a proof-of-concept for its potential utility in predicting patient-specific medications

Autism and ADHD Symptoms in Patients with OCD: Are They Associated with Specific OC Symptom Dimensions or OC Symptom Severity?

In obsessive-compulsive disorder (OCD), the relationship between autism spectrum disorders (ASD), attention-deficit/hyperactivity disorder (ADHD) symptom, and obsessive-compulsive (OC) symptom dimensions and severity has scarcely been studied. Therefore, 109 adult outpatients with primary OCD were compared to 87 healthy controls on OC, ADHD and ASD symptoms. OCD patients showed increased ADHD and autism symptom frequencies, OCD + ADHD patients reporting more autism symptoms (particularly attention switching and social skills problems) than OCD − ADHD patients. Attention switching problems were most significant predictors of OC symptom dimensions (except hoarding) and of symptom severity. Hoarding was not associated with elevated autism scale scores, but with inattention. In conclusion, attention switching problems may reflect both symptom overlap and a common etiological factor underlying ASD, ADHD and OCD

Antibiotic Restriction Might Facilitate the Emergence of Multi-drug Resistance

<div><p>High antibiotic resistance frequencies have become a major public health issue. The decrease in new antibiotics' production, combined with increasing frequencies of multi-drug resistant (MDR) bacteria, cause substantial limitations in treatment options for some bacterial infections. To diminish overall resistance, and especially the occurrence of bacteria that are resistant to all antibiotics, certain drugs are deliberately scarcely used—mainly when other options are exhausted. We use a mathematical model to explore the efficiency of such antibiotic restrictions. We assume two commonly used drugs and one restricted drug. The model is examined for the mixing strategy of antibiotic prescription, in which one of the drugs is randomly assigned to each incoming patient. Data obtained from Rabin medical center, Israel, is used to estimate realistic single and double antibiotic resistance frequencies in incoming patients. We find that broad usage of the hitherto restricted drug can reduce the number of incorrectly treated patients, and reduce the spread of bacteria resistant to both common antibiotics. Such double resistant infections are often eventually treated with the restricted drug, and therefore are prone to become resistant to all three antibiotics. Thus, counterintuitively, a broader usage of a formerly restricted drug can sometimes lead to a decrease in the emergence of bacteria resistant to all drugs. We recommend re-examining restriction of specific drugs, when multiple resistance to the relevant alternative drugs already exists.</p></div

Clinical implications of high-sensitivity cardiac troponin measurements in hospitalized medical patients.

BACKGROUND:The increased use of high sensitivity cardiac troponins (hs-cTn), have made the diagnosis of non-ST elevation myocardial infarction (MI) challenging, especially in complex medical patients, in whom the clinical presentation of MI is nonspecific and multiple comorbidities as well as non-ischemic acute conditions may account for elevated hs-cTn levels. The aim of this study was to assess the frequency of both elevated hs-cTn levels and dynamic changes in hospitalized patients. METHODS AND FINDINGS:We conducted a retrospective study identifying all patients hospitalized in the Internal Medicine Division of Rabin Medical Center, Israel between January 2011 to December 2011, for whom at least one hs-cTn T (hs-cTnT) measurement was obtained. Collected data included patient demographics, acute and chronic diagnosis, hs-cTnT and creatinine levels and date of death. Hs-cTnT levels were obtained in 5,696 admissions and was above the 99th percentile (> = 13 ng/L) in 61.6% of the measurements. A relative change of 50% or higher was observed in 24% of the admissions. Among those with elevated hs-cTnT levels, acute coronary syndromes (ACS) accounted for only 6.1% of acute diagnoses. Maximal hs-cTnT levels above 100 ng/L but not dynamic changes discriminated between ACS and non-ACS conditions (positive and negative predictive values of 12% and 96% respectively). The frequency of elevated hs-cTnT levels was age-dependent and over 75% of patients aged >70 years-old had levels above the 99th percentile. Multivariate analysis identified hs-cTnT levels higher than the 99th percentile, as an independent, strong predictor for 30-day mortality (OR 4.58 [2.8, 7.49], p<0.0001). CONCLUSIONS:Elevated hs-cTnT levels together with dynamic changes are frequent findings among hospitalized patients and in most cases, are not related to the ACS diagnosis. These findings highlight the diagnostic challenge of ACS in this complex population. Further studies are needed in order to optimize the use of hs-cTnT measurements in hospitalized patients

The mixing strategy for varying double resistance frequencies.

<p>We measure the fraction of incorrect treatment (A), and the rate of triple resistance emergence (B) for varying levels of double resistance to the commonly used antibiotics (</p><p></p><p></p><p></p><p><mi>f</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>2</mn></p><p></p><p></p><p></p><p></p><p></p><p></p>). Red curves represent the results under mixing 2 antibiotics and restricting antibiotic 3 (<i>mix</i>2), while green curves are the results under mixing 3 antibiotics (<i>mix</i>3). Dotted and solid lines are the results of the model with and without community feedback, respectively. Parameters are <p></p><p></p><p></p><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>1</mn></p><p></p><p></p><mo>=</mo><mo> </mo><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>2</mn></p><p></p><p></p><mo>=</mo><mn>0.1</mn><mo>,</mo><mi>β</mi><mo>=</mo><mn>0.3</mn><mo>,</mo><mo> </mo><p><mi>λ</mi><mi>X</mi></p><mo>=</mo><mn>0.07</mn><mo>,</mo><p><mi>p</mi><mn>1</mn></p><mo>=</mo><p><mi>p</mi><mn>2</mn></p><mo>=</mo><p><mi>p</mi><mn>3</mn></p><mo>=</mo><mn>0.07</mn><p></p><p></p><p></p><p></p><p></p><p><mo> </mo></p><p><mi>λ</mi><mi>S</mi></p><mo>=</mo><mi>c</mi><mo>−</mo><p><mi>λ</mi><mi>X</mi></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi><mn>1</mn></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi><mn>2</mn></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi><mn>3</mn></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>2</mn></p><p></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>3</mn></p><p></p><p></p><p></p><mo>−</mo><p><mi>λ</mi></p><p></p><p><mi>R</mi></p><p><mn>2</mn><mo>,</mo><mn>3</mn></p><p></p><p></p><p></p><p></p><p></p><p></p>, and the rest are given at <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.t001" target="_blank">Table 1</a>. The system is simulated for 20 years and other parameter values are given in the text.<p></p

An illustration of the dynamic system presented in Eq 1.

<p>The black frame represents the hospital, with hollowed arrows signifying patients moving in and out between the hospital and the community; circles representing patient frequencies within the hospital, and squares representing the frequencies of patients infected with bacterial strains in the community, with respect to variable names within the shapes (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#sec002" target="_blank">methods</a>). Colored arrows show the direction of resistance acquisition due to treatment; solid black arrows are recovery from infected to cleared states, while dashed lines are infections. Several arrows are marked with the corresponding rates of flow between variables.</p

Time series of <i>mix</i>2 (A) and <i>mix</i>3 (B).

<p>We plot the frequencies of double resistant infections resistant to the third antibiotic (<i>R</i>_1,3 + <i>R</i>_2,3), the double resistant infections resistant to the two commonly used antibiotics (<i>R</i>_1,2), the measured incorrectly treated patients, and the emergence of triple resistance. The model is simulated for an extended period of time (100 years) to capture long term effects and the rest of the parameters are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.g002" target="_blank">Fig 2</a>.</p

The mixing strategy for estimated resistance frequencies.

<p>For each data point, its location on the X axis represents the predicted ratio of incorrect treatment under <i>mix</i>3 relative to <i>mix</i>2 according to our model, and its location on the Y axis represents the predicted ratio of triple resistance emergence. A red line is drawn where the strategies inhibit triple resistance equally well, so below the line <i>mix</i>3 reduces both incorrect treatment and triple resistance emergence more efficiently than <i>mix</i>2. Panels A and B present the results of the models without community feedback and with it, respectively. Antibiotic resistance frequencies among incoming patients are estimated from data (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#sec002" target="_blank">Methods</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.s006" target="_blank">S1 Table</a>). The color indicates the estimated resistance frequencies to the common antibiotics (</p><p></p><p></p><p></p><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>1</mn></p><p></p><p></p><mo>+</mo><p><mi>f</mi></p><p></p><p><mi>R</mi><mn>2</mn></p><p></p><p></p><mo>+</mo><p><mi>f</mi></p><p></p><p><mi>R</mi></p><p><mn>1</mn><mo>,</mo><mn>2</mn></p><p></p><p></p><p></p><p></p><p></p><p></p>). The system is simulated for 20 years and the rest of the parameters are as in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004340#pcbi.1004340.g002" target="_blank">Fig 2</a>.<p></p

Parameters, their meaning and values.

<p>Parameters, their meaning and values.</p