lchicine has a higher value than silymarin . An in vitro efficacy predictor Epredict that positively correlates with the Ein vivo value of a drug The SAUC values for the majority of drugs showed a weak positive correlation with the Ein vivo. We investigated if we could further enhance this correlation by applying weights to the SAUC values. 0 
indicates no contribution of the marker to the positive correlation; while 2 indicates strong contribution of the marker to the positive correlation. The Ein vivo values from the CCl4 treatment model were used as the training dataset to find the optimized weights. All possible linear combinations of the 3 weights with 10 markers were 21358117” subjected to the Spearman’s rank Animal models DMN induced fibrosis Drugs silymarin thalidomide tetrandrine colchicine histological score 2 1.56 2 3.8 3.4 3.84 3.76 3.4 4 3.33 3.6 3.03 3.38 3.76 4 2.91 3.58 4 histological score 1.6 0.89 1.3 2.3 2.5 2.8 2.67 2.1 2.63 1.33 3.68 1.87 2.25 2.43 2.5 0.76 ” 1.5 1.94 Ein vivo: xSc 0.8 1 1.4 5.7 3.1 4 4.1 4.4 5.5 6.7 20.3 3.5 3.8 5 6 6.3 7.4 8.2 CCl4 induced fibrosis silymarin 5-Pregnen-3b-ol-20-one-16a-carbonitrile malotilate rosmarinic acid pioglitazone taurine CCl4 induced fibrosis PCN taurine melatonin oxymatrine silymarin malotilate EGCG pioglitazone All data are taken from the literature using dimethylnitrosamine treatment, carbon tetrachloride treatment, or CCl4 preventive fibrotic rat models. Histological scores are linearly converted to a scale from 0 to 4. Ein vivo is established as shown. Drugs under each animal model are sorted according to increasing Ein vivo. Silymarin, malotilate and pioglitazone have the same relative ranking in CCl4 treatment and preventive models. doi:10.1371/journal.pone.0026230.t001 6 November 2011 | Volume 6 | Issue 11 | e26230 Ranking Anti-Fibrotic Drugs correlation test against Ein vivo from CCl4 fibrosis model. One outlier was allowed in the analysis, as the sample size is relatively small. The Spearman’s rank correlation coefficient rho ranges from 0 to 1, where 1 means perfect rank correlation, and 0 means the opposite order. The optimized weight for each marker was determined to be the value with the highest frequency occurrence out of all cases which achieved rho = 1. High weight implies high importance of the marker towards a strongly positive correlation. The optimized weights yielded the following efficacy predictor, computed as the linear combination of the 10 optimized weights with the SAUC values: Epredict ~SAUCDHE zSAUCcollagen III z2|SAUCmitochondrial membrane potential z2|SAUCTIMP. 29. Oakley F, Meso M, Iredale JP, Green K, Marek CJ, et al. Inhibition of inhibitor of kappaB purchase 2883-98-9 kinases stimulates hepatic stellate cell apoptosis and accelerated recovery from rat liver fibrosis. Gastroenterology 128: 108120. 30. de Gouville AC, Boullay V, Krysa G, Pilot J, Brusq JM, et al. Inhibition of TGF-beta signaling by an ALK5 inhibitor protects rats from dimethylnitrosamine-induced liver fibrosis. Br J Pharmacol 145: 166177. 31. Soma J, Sugawara T, Huang YD, Nakajima J, Kawamura M Tranilast slows the progression of advanced diabetic nephropathy. Nephron 92: 693698. 32. van Rossum TG, Vulto AG, Hop WC, Schalm SW Glycyrrhizin-induced reduction of ALT in European patients with chronic hepatitis C. Am J Gastroenterol 96: 24322437. 33. Gressner AM, Weiskirchen R Modern pathogenetic concepts of liver fibrosis suggest stellate cells and TGF-beta as major players and therapeutic targets. J Cell Mol
