A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria.

BACKGROUND: Nearly half of the world's population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. METHODS: The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. RESULTS: One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. CONCLUSIONS: Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner

Prediction and identification of synergistic compound combinations against pancreatic cancer cells.

Resistance to current therapies is common for pancreatic cancer and hence novel treatment options are urgently needed. In this work, we developed and validated a computational method to select synergistic compound combinations based on transcriptomic profiles from both the disease and compound side, combined with a pathway scoring system, which was then validated prospectively by testing 30 compounds (and their combinations) on PANC-1 cells. Some compounds selected as single agents showed lower GI50 values than the standard of care, gemcitabine. Compounds suggested as combination agents with standard therapy gemcitabine based on the best performing scoring system showed on average 2.82-5.18 times higher synergies compared to compounds that were predicted to be active as single agents. Examples of highly synergistic in vitro validated compound pairs include gemcitabine combined with Entinostat, thioridazine, loperamide, scriptaid and Saracatinib. Hence, the computational approach presented here was able to identify synergistic compound combinations against pancreatic cancer cells

Transcriptional drug repositioning and cheminformatics approach for differentiation therapy of leukaemia cells.

Differentiation therapy is attracting increasing interest in cancer as it can be more specific than conventional chemotherapy approaches, and it has offered new treatment options for some cancer types, such as treating acute promyelocytic leukaemia (APL) by retinoic acid. However, there is a pressing need to identify additional molecules which act in this way, both in leukaemia and other cancer types. In this work, we hence developed a novel transcriptional drug repositioning approach, based on both bioinformatics and cheminformatics components, that enables selecting such compounds in a more informed manner. We have validated the approach for leukaemia cells, and retrospectively retinoic acid was successfully identified using our method. Prospectively, the anti-parasitic compound fenbendazole was tested in leukaemia cells, and we were able to show that it can induce the differentiation of leukaemia cells to granulocytes in low concentrations of 0.1 μM and within as short a time period as 3 days. This work hence provides a systematic and validated approach for identifying small molecules for differentiation therapy in cancer

Polynomial time solution to NP-complete problem Hamiltonian cycle (P=NP Proof)

P vs NP is one of the open and most important mathematics/computer science questions that has not been answered since it was raised in 1971 despite its importance and a quest for a solution since 2000. P vs NP is a class of problems that no polynomial time algorithm exists for any. If any of the problems in the class gets solved in polynomial time, all can be solved as the problems are translatable to each other. One of the famous problems of this kind is Hamiltonian cycle. Here we propose a polynomial time algorithm with rigorous proof that it always finds a solution if there exists one. It is expected that this solution would address all problems in the class and have a major impact in diverse fields including computer science, engineering, biology, and cryptography

電腦輔助引子設計之裝置與方法以及偵測嚴重急性呼吸道症候群冠狀病毒之組合物

[[abstract]]本發明揭露以熱力動力學理論為基礎進行引子之適用性評估的方法，以及實施上述方法之電腦程式或網路介面裝置。本發明亦提供包括引子序列之組合物，以用作為偵測SARS-CoV之引子對或巢式引子組。The invention describes methods based on thermodynamic theory for evaluating the fitness of primers, and a computer program or web-interface apparatus for implementing these methods. The invention also provides compositions comprising ptimer sequences for use as primer pairs or nested primer sets for detecting SARS-CoV

[[alternative]]Thiourea compounds and method for inhibiting hepatitis c virus infection

[[abstract]]本發明提供一種硫尿化合物，具有下列化學式(I)：其中每一R1 、R2 與R3 獨立地包括氫、C1 －C10 烷基、C2 －C10 烯基、C2 －C10 炔基、C3 －C20 環烷基、C3 －C20 環烯基、C1 －C20 異環烷基、C1 －C20 異環烯基、芳基或異芳基或R1 與R2 與一氮原子鍵結形成C3 －C20 異環烷基或R2 與R3 與二氮原子鍵結形成C3 －C20 異環烷基；每一A1 與A2 獨立地包括芳基或異芳基；每一X、Y與Z獨立地包括O、S、S(O)、S(O)2 、N(Ra )、C(Ra Rb )、C1 －C10 烷基、C2 －C10 烯基、C2 －C10 炔基、C3 －C20 環烷基、C1 －C20 異環烷基、芳基或異芳基，每一Ra 與Rb 獨立地包括氫、C1 －C10 烷基、C3 －C20 環烷基、C1 －C20 異環烷基、芳基或異芳基；每一m與n獨立地為1、2、3、4或5；以及每一x、y與z獨立地為0或1。該硫尿化合物可用於抑制C型肝炎病毒感染。A thiourea compound of formula (I) is provided,wherein each of R1 , R2 and R3 , independently, is H, C1 -C10 alkyl, C2 -C10 alkenyl, C2 -C10 alkynyl, C3 -C20 cycloalkyl, C3 -C20 cycloalkenyl, C1 -C20 heterocycloalkyl, C1 -C20 heterocycloalkenyl, aryl or heteroaryl, or R1 and R2 , together with the nitrogen atom to which they are bonded, are C3 -C20 heterocycloalkyl, or R2 and R3 , together with the two nitrogen atoms to which they are bonded and the carbon atom bonded to both of the two nitrogen atoms, are C3 -C20 heterocycloalkyl, each of A1 and A2 , independently, is aryl or heteroaryl, each of X, Y, and Z, independently, is O, S, S(O), S(O)2 , N(Ra ), C(Ra Rb ), C1 -C10 alkyl, C2 -C10 alkenyl, C2 -C10 alkynyl, C3 -C20 cycloalkyl, C1 -C20 heterocycloalkyl, aryl or heteroaryl, in which each of Ra and Rb , independently, is H, C1 -C10 alkyl, C3 -C20 cycloalkyl, C1 -C20 heterocycloalkyl, aryl or heteroaryl, each of m and n, independently, is 1, 2, 3, 4 or 5, and each of x, y and z, independently, is 0 or 1. The compound can be used to inhibit hepatitis C virus infection

MOESM3 of A systematic and prospectively validated approach for identifying synergistic drug combinations against malaria

Additional file 3: Figure S2. Pathway enrichment for selected predicted compounds. a) The Figure represents the amount of enrichment (Z-score) of each pathway. The Y axis represents the pathway indices of pathways listed in Additional file 4: Table S1