Abstract: Methods are provided for predicting the effect of a drug given the drug dose and individual patient clinical characteristics. A neural network is trained on samples of clinical data including the observed drug dose and effect on patients, as well as their individual clinical characteristics. The neural network is then validated to ensure that its predictions fall within an acceptable error range. The neural network is used to predict the effect of a given drug dose for a given set of individual patient clinical characteristics. Methods are also provided for predicting the drug dose required to achieve a desired effect. Another neural network is trained on samples of clinical data including the observed drug dose and effect on patients, as well as their individual clinical characteristics. The neural network is then validated to ensure that its predictions fall within an acceptable error range.

Abstract: The invention relates to the discovery that a putative gene of Mycobacterium tuberculosis with no previously identified function is responsible for the ability of the bacterium to activate thioamide drugs. Since M. tuberculosis has a low rate of synonymous mutations, all mutations in this gene, identified as Rv3854c and now termed “EtaA,” are expected to inhibit the ability of a bacterium with the mutation to activate a thioamide or thiocarbonyl drug. Thus, detecting a bacterium with a mutation in this gene indicates that the bacterium is resistant to treatment with thioamides.

Abstract: The present invention relates, in general, to a adenovirus mediated transfer of genes to the gastrointestinal tract. In particular, the present invention relates to a method of recombinant, replication-deficient adenovirus mediated transfer of therapuetic genes to the gastrointestinal tract whereby therapeutic proteins for systemic and/or local purposes are produced.