Identification of a battery of tests for drug candidate evaluation in the SMNDelta7 neonate model of spinal muscular atrophy.

Spinal muscular atrophy (SMA) is characterized by selective loss of alpha-motor neurons and is caused by homozygous loss or mutation in the survival motor neuron (SMN1) gene. Loss of SMN1 is partially compensated by the copy gene, SMN2. Currently, there are no specific treatments for SMA. Key features of SMA are modeled in mice by deletion of murine Smn, and insertion of both full length human SMN2 gene and the major aberrant splice isoform of the SMN2 gene (SMNDelta7; [Le, T.T., Pham, L.T., Butchbach, M.E., Zhang, H.L., Monani, U.R., Coovert, D.D., Gavrilina, T.O., Xing, L., Bassell, G.J., and Burghes, A.H. 2005. SMNDelta7, the major product of the centromeric survival motor neuron (SMN2) gene, extends survival in mice with spinal muscular atrophy and associates with full-length SMN. Hum Mol Genet 14: 845-857]). The present study identified moderate-throughput, quantitative behavioral tests in neonatal SMN2(+/+);SMNDelta7(+/+);Smn(-/-) mice. It also addresses methodological approaches and common interpretational challenges in a neonatal model with motor deficiencies and frequent deaths. Animals were assessed daily for body weight and survival, and every other day for neonatal well-being indices and tests of motor function such as performance on the hind-limb suspension test (a.k.a. tube test) and geotaxis. The tube test is a novel non-invasive motor function test specifically designed for neonatal rodents. We found progressive deterioration in SMA model mice for most measures studied particularly body weight, survival, body temperature and motor function with differences appearing as early as P3. Power analysis showed that body weight, survival, righting reflex, geotaxis and tube test had highest predictive power for drug efficacy studies. This multi-functional component battery of tests provides a rapid and efficient means to identify, evaluate and develop candidate therapies as a prelude to human clinical trials.

CSF quinolinic acid levels are determined by local HIV infection: cross-sectional analysis and modelling of dynamics following antiretroviral therapy.

Quinolinic acid (QUIN) is a product of tryptophan metabolism that can act as an endogenous brain excitotoxin when released by activated macrophages. Previous studies have shown correlations between increased CSF QUIN levels and the presence of the AIDS dementia complex (ADC), a neurodegenerative condition complicating late-stage human immunodeficiency virus type 1 (HIV) infection in some patients. CSF QUIN is putatively one of the important molecular mediators of the brain injury in this clinical setting and, more generally, serves as a marker of local macrophage activation. This study was undertaken to examine the relationship of CSF QUIN concentrations to local HIV infection and to define the effects of antiretroviral drug treatment on CSF QUIN using two complementary approaches. The first was an exploratory cross-sectional analysis of a clinically heterogeneous sample of 62 HIV-infected subjects, examining correlations of CSF QUIN levels with CSF and plasma HIV RNA levels and other salient parameters of infection. The second involved longitudinal observations of a subset of 20 of these subjects who initiated new antiretroviral therapy regimens. In addition to descriptive analysis, we used kinetic modelling of QUIN decay in relation to that of HIV RNA to assess further the relationship between CSF QUIN and infection in the dynamic setting of treatment. The cross-sectional studies showed strong correlations of CSF QUIN with both CSF HIV RNA and blood QUIN levels, as well as with elevations in CSF white blood cells, CSF total protein and CSF:blood albumin ratio. In this group of subjects with a low incidence of active, untreated ADC, CSF QUIN did not correlate with ADC stage or measures of quantitative neurological performance. Antiviral treatment reduced the CSF QUIN levels in all the longitudinally followed, treated subjects. Kinetic modelling of CSF QUIN decay indicated that CSF QUIN levels were driven primarily by CSF HIV infection with a lesser contribution from blood QUIN levels. In three subjects with new-onset, untreated ADC, CSF QUIN decay paralleled both CSF HIV decrement and improvement in neurological performance. These studies show that CSF QUIN concentrations relate primarily to active CSF HIV infection and to a lesser extent to plasma QUIN. CSF QUIN serves as a marker of local infection with a wide dynamic range. The time course of therapy-induced changes links CSF QUIN to local infection and supports the action of antiviral therapy in ameliorating immunopathological brain injury and ADC.

Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro.

Assays of drug action typically evaluate biochemical activity. However, accurately matching therapeutic efficacy with biochemical activity is a challenge. High-content cellular assays seek to bridge this gap by capturing broad information about the cellular physiology of drug action. Here, we present a method of predicting the general therapeutic classes into which various psychoactive drugs fall, based on high-content statistical categorization of gene expression profiles induced by these drugs. When we used the classification tree and random forest supervised classification algorithms to analyze microarray data, we derived general "efficacy profiles" of biomarker gene expression that correlate with anti-depressant, antipsychotic and opioid drug action on primary human neurons in vitro. These profiles were used as predictive models to classify naïve in vitro drug treatments with 83.3% (random forest) and 88.9% (classification tree) accuracy. Thus, the detailed information contained in genomic expression data is sufficient to match the physiological effect of a novel drug at the cellular level with its clinical relevance. This capacity to identify therapeutic efficacy on the basis of gene expression signatures in vitro has potential utility in drug discovery and drug target validation.