ABSTRACT
Valorphin (V1) is a naturally occurring peptide derived from hemoglobin that has been found to have an affinity for opioid receptors and exhibits antinociceptive and anticonvulsant activity. Some of its synthetic analogs containing an aminophosphonate moiety show structure-dependent potent antinociceptive effects. This study aimed to reveal a detailed picture of the antinociceptive mechanisms and behavioral effects of V1 and its recently synthesized phosphopeptide analog V2p in rodents using a range of methods. The studied peptides significantly reduced acute (mean V1-9.0, V2p-5.8 vs. controls-54.1 s) and inflammatory (mean V1-57.9 and V2p-53.3 vs. controls-107.6 s) nociceptive pain in the formalin test, as well as carrageenan-induced hyperalgesia (mean V1-184.7 and V2p-107.3 vs. controls-61.8 g) in the paw pressure test. These effects are mediated by activation of opioid receptors with a predominance of kappa in V1 antinociception and by delta, kappa, and mu receptors in V2p-induced antinociception. Both peptides did not change the levels of TNF-alpha and IL-1-beta in blood serum. V1 induces depression-like behavior, and V2p shows a tendency toward anxiolysis and short-term impairment of motor coordination without affecting exploratory behavior. The results characterize valorphin and its derivative as promising analgesics that exert their effects both centrally and peripherally, without causing severe behavioral changes in experimental animals. These encouraging data are a foundation for future studies focusing on the effects of hemorphins after long-term treatment.
ABSTRACT
Genomic aberrations and gene expression-defined subtypes in the large METABRIC patient cohort have been used to stratify and predict survival. The present study used normalized gene expression signatures of paclitaxel drug response to predict outcome for different survival times in METABRIC patients receiving hormone (HT) and, in some cases, chemotherapy (CT) agents. This machine learning method, which distinguishes sensitivity vs. resistance in breast cancer cell lines and validates predictions in patients; was also used to derive gene signatures of other HT (tamoxifen) and CT agents (methotrexate, epirubicin, doxorubicin, and 5-fluorouracil) used in METABRIC. Paclitaxel gene signatures exhibited the best performance, however the other agents also predicted survival with acceptable accuracies. A support vector machine (SVM) model of paclitaxel response containing genes ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2, SLCO1B3, TUBB1, TUBB4A, and TUBB4B was 78.6% accurate in predicting survival of 84 patients treated with both HT and CT (median survival ≥ 4.4 yr). Accuracy was lower (73.4%) in 304 untreated patients. The performance of other machine learning approaches was also evaluated at different survival thresholds. Minimum redundancy maximum relevance feature selection of a paclitaxel-based SVM classifier based on expression of genes BCL2L1, BBC3, FGF2, FN1, and TWIST1 was 81.1% accurate in 53 CT patients. In addition, a random forest (RF) classifier using a gene signature ( ABCB1, ABCB11, ABCC1, ABCC10, BAD, BBC3, BCL2, BCL2L1, BMF, CYP2C8, CYP3A4, MAP2, MAP4, MAPT, NR1I2,SLCO1B3, TUBB1, TUBB4A, and TUBB4B) predicted >3-year survival with 85.5% accuracy in 420 HT patients. A similar RF gene signature showed 82.7% accuracy in 504 patients treated with CT and/or HT. These results suggest that tumor gene expression signatures refined by machine learning techniques can be useful for predicting survival after drug therapies.
