ライフサイエンスコーパス: drug combination

1 redictive of preclinical sensitivity to this drug combination.

2 1047R) tumor-bearing mice long term with the drug combination.

3 ry role for ROS in the efficacy of the three drug combination.

4 evious analyses showing the efficacy of this drug combination.

5 and CD8+ cells in tumours receiving adjuvant drug combination.

6 fit from the single most effective drug in a drug combination.

7 he combinatorial complexity of screening all drug combinations.

8 velop resistance that might be overcome with drug combinations.

9 Prediction Challenge to predict synergistic drug combinations.

10 TOP2 poison and thus reduce the efficacy of drug combinations.

11 ed corresponding synergistic or antagonistic drug combinations.

12 n of new RXRalpha-based antitumor agents and drug combinations.

13 e metric for assessing the efficacy of novel drug combinations.

14 matologic malignancies against a panel of 48 drug combinations.

15 means to explore the large space of possible drug combinations.

16 of patient sensitivity to various drugs and drug combinations.

17 lls and translated these hits into effective drug combinations.

18 alog efficacy through prodrug strategies and drug combinations.

19 emcitabine, we could identify more effective drug combinations.

20 bination drug therapy, including non-obvious drug combinations.

21 s across seven different clinically relevant drug combinations.

22 superior to that of classical two- or three-drug combinations.

23 ynamic model for both the binary and ternary drug combinations.

24 d to the formidable challenges of optimizing drug combinations.

25 cal trial by assessing potentially hazardous drug combinations.

26 for high-throughput screening of anticancer drug combinations.

27 peed up the entire discovery cycle of potent drug combinations.

28 principle-based reward for disease-specific drug combinations.

29 ug sensitivity, and helps identify effective drug combinations.

30 esents a novel strategy for designing cancer drug combinations.

31 tage to cancer cells against clinically used drug combinations.

32 immediate significance for the design of new drug combinations.

33 technique for identifying AEs caused by two-drug combinations.

34 the development of rational, mechanism-based drug combinations.

35 udy the effect of target tissue exposure for drug combinations.

36 ent (SDD) category for certain organisms and drug combinations.

37 ity and resistance for a total of 900 mutant-drug combinations.

38 eatment of preexisting CSCs with a genotoxic drug combination (5-fluorouracil, doxorubicin, and cyclo

39 Drug combinations acting synergistically to kill cancer

40 table framework for the design of high-order drug combinations against any pathogen or tumor.

41 We applied this framework to find the best 4-drug combinations against drug-resistant Mtb by adding n

42 The drug combination also reduced PC xenograft tumor burden

43 sed drugs as compared with oral antidiabetic-drug combinations among patients with a history of heart

44 models for identification of Effective Multi-drug combinations), an online tool kit that can effectiv

45 A key step in drug combination analysis is the selection of an additiv

46 were administered orally and doses varied by drug combination and site.

47 how that the macaques that had received this drug combination and then stopped antiretroviral therapy

48 ing investigations, including fixed-duration drug combinations and alternative dosing schedules, are

49 ically focus on individual drugs rather than drug combinations and animal models are unreliable.

50 suitable for systematically testing various drug combinations and clinical trial designs in solid ca

51 suggest further, large-scale trials of these drug combinations and consideration of their use in STH

52 r the disease module similar to FDA-approved drug combinations and could potentially suggest novel sy

53 ugs are in pre-clinical development as novel drug combinations and immunotherapy combinations for can

54 e, to reduce the adverse effects of multiple drug combinations and improve outcome, we proposed a sin

55 development of DCCT strategies for different drug combinations and ratios.

56 To maximize therapeutic benefit, drug combinations and schedules must be explored to iden

57 lon-cancer metastasis can identify effective drug combinations and that the model has future clinical

58 evaluating the efficacy of antituberculosis drug combinations and the gaps in the evidence base for

59 elucidates a method to identify synergistic drug combinations and translate them to in vivo by prese

60 ned from prior losses of treatment efficacy, drug combinations, and control strategies will help adva

61 determining the optimal use of PARPi within drug combination approaches has been challenging.

62 egies may help provide rationales for future drug combination approaches with antimetastatic agents t

63 This drug combination (AraC+CHK1i+G-CSF) will open the doors

64 Synergistic drug combinations are a promising approach to achieve a

65 ople.IMPORTANCE A growing number of anti-HIV drug combinations are effective in suppressing virus rep

66 Data from applied drug combinations are input into the differential evolut

67 Rather, drug combinations are largely dictated by empirical or c

68 Our results illustrate how high-order drug combinations are needed to kill drug-resistant canc

69 However, 20% of drug combinations are poorly predicted by all methods.

70 Many drug combinations are routinely assessed to identify syn

71 onal identification methods of the effective drug combinations are usually associated with significan

72 Drug combinations are valuable tools for studying biolog

73 ients were given the recommended dose of the drug combination as determined from the dose-escalation

74 and that the model can be used to determine drug combinations associated with superior tumour killin

75 has been developed that selectively delivers drug combinations at synergistic ratios via tumor-target

76 eatment for human babesiosis consists of two drug combinations, atovaquone + azithromycin or quinine

77 esent an approach to uncover the efficacy of drug combinations based on the analysis of mono-drug eff

78 ssion of RCC, we identified the best overall drug combination, being a mixture of four drugs (axitini

79 r signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded

80 at permit relative comparisons among various drug combinations by quantification of synergistic activ

81 Therefore, we propose that drug combination can be employed not only for increasing

82 prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple

83 ce evolution of CTX-M-15, and illustrate how drug combinations can be rationally designed to limit th

84 Three-drug combinations can control hypertension in about 90%

85 Synergistic drug combinations can lessen potential toxic side effect

86 Use of rational drug combinations can overcome resistance to targeted dr

87 Polytherapy (or drug combination cancer therapy (DCCT)), targeting multi

88 ypophosphorylation of 4E-BP1 induced by this drug combination causes an arrest of protein synthesis,

89 n/paclitaxel/bevacizumab, platinum-based two-drug combination chemotherapy, or non-platinum-based two

90 t credentialing and validation, implementing drug combinations, clinical trial designs, targeting tum

91 The generated drug combinations collectively cover the disease module

92 a decreased flux across Caco-2 cells for the drug combinations compared to drug alone.

93 Even as the clinical impact of drug combinations continues to accelerate, no consensus

94 demonstrate proof-of-concept that candidate drug combinations could significantly inhibit growth and

95 stical modeling of single drug response from drug combination data can help determine significance of

96 Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experimen

97 Most importantly, the drug combination depletes FLT3/ITD(+) LSCs in a genetic

98 andomly assigned to receive ART (an approved drug combination derived from US Department of Health an

99 eveloped the first deep generative model for drug combination design, by jointly embedding graph-stru

100 r small-molecule combinations, computational drug-combination design has not seen generative models t

101 learned representations, we have recast the drug-combination design problem as graph-set generation

102 are uncommon and nearly always involve multi-drug combinations developed by experimentation in humans

103 and albendazole [IDA]) is superior to a two-drug combination (diethylcarbamazine plus albendazole [D

104 otential to accelerate resistance-overcoming drug combination discovery.

105 Lifelong antiretroviral (ARV) drug combination dosing allows management as a chronic c

106 d also allow ready tailoring of a particular drug combination/drug release for the needs of an indivi

107 This drug combination effectively reduced the proliferation o

108 isualization, analysis and quantification of drug combination effects in terms of synergy and/or anta

109 eening is the standard approach to study the drug combination effects, yet it becomes impractical whe

110 dependence models of interaction to evaluate drug combination effects.

111 ombo predictions closely agree with measured drug combination efficacies both in vitro (Pearson's cor

112 nd CLL revealed specific patterns of ex vivo drug combination efficacy that were associated with sele

113 ta into clinically meaningful predictions of drug combination efficacy.

114 Even when drug combinations exhibit additivity or synergy in pre-c

115 Furthermore, cells treated with the drug combination exhibited increased promoter and gene b

116 how that the deep generative models generate drug combinations following the principle across disease

117 We evaluated the safety and efficacy of the drug combination for 26 weeks in patients with extensive

118 rs of proteasome and HSPAs seem an effective drug combination for pre-clinical development in highly

119 isplatin and paclitaxel, the clinically used drug combination for the treatment of advanced ovarian c

120 talk inhibition, to discover new synergistic drug combinations for breast cancer treatment.

121 approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mecha

122 tional design of nanomedicine of synergistic drug combinations for cancer therapy.

123 cal approach in identifying synthetic lethal drug combinations for cancer treatment.

124 we prioritize approximately 10(5) human TSG-drug combinations for future follow-up.

125 Relying on approved drug combinations for hypertension and cancer, we find t

126 are driven by the availability of drugs and drug combinations for initial therapy of myeloma as well

127 thodology to identify clinically efficacious drug combinations for specific diseases.

128 a protocol for the discovery of synergistic drug combinations for the treatment of disease.

129 cterial death is essential to develop potent drug combinations for the treatment of tuberculosis.

130 Nanomedicine of synergistic drug combinations has shown increasing significance in c

131 eatment of HIV infections 10 tenofovir-based drug combinations have been marketed, and tenofovir diso

132 Drug combinations have the potential to improve efficacy

133 of how to engineer rational, mechanism-based drug combinations, however, remains lacking.

134 In addition, the drug combination (i.e. lapatinib plus YM155) decreased n

135 er-soluble anticancer agents and delivery of drug combinations (i.e. multi-drug delivery) that seeks

136 From cellular activation to drug combinations, immunological responses are shaped by

137 cannot coordinate the specific delivery of a drug combination in an accurately tuned ratio into cance

138 ounds of assaying to identify an optimal tri-drug combination in eight distinct chemoresistant bladde

139 anoid libraries in evaluating inhibitors and drug combinations in a preclinical setting.

140 sis to efficiently generate disease-specific drug combinations in a vast chemical combinatorial space

141 and synergies to identify the most effective drug combinations in advanced cancer models, thereby imp

142 viability responses of many single drug and drug combinations in agreement with experimental data.

143 ROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.

144 the use of Rgs16::GFP expression for testing drug combinations in cell culture and validation of best

145 Drug combinations in experimental models restore crenola

146 rongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks o

147 an effective strategy to screen synergistic drug combinations in high-throughput and a CRISPR-based

148 explain the superiority of many FDA-approved drug combinations in the absence of drug synergy or addi

149 ameters that predict the in vivo outcomes of drug combinations in the highly aggressive orthotopic 4T

150 s as either single agents or in 768 pairwise drug combinations in TNBC cell lines to identify synergi

151 The current approach to designing drug combinations includes in vitro optimization to maxi

152 ngevity in Drosophila Remarkably, the triple drug combination increased lifespan by 48%.

153 es were performed to test different drugs or drug combination, indicating that 5-fluorouracil (5-FU)

154 The drug combination induced synergistic inhibition of proli

155 The drug combination inhibited DNMT3a protein levels and inc

156 omosomal instability and in synthetic lethal drug combinations inspire optimism that CDK inhibitors w

157 and which one is dominant for a given tumor-drug combination is not known.

158 strate that the task of predicting AEs for 2-drug combinations is amenable to the Likelihood Ratio Te

159 The discovery of anti-resistance drug combinations is challenging as resistance can arise

160 However, in vivo translatability of drug combinations is complicated by the disparities in d

161 Therefore, identification of effective drug combinations is desperately needed.

162 Although the number of possible drug combinations is extensive, a series of principles c

163 ancers, but direct screening of all possible drug combinations is infeasible.

164 However, the search space of drug combinations is large, making the identification of

165 Our ability to discover effective drug combinations is limited, in part by insufficient un

166 Synergistic drug combinations lead to the use of drugs at lower dose

167 identify the positive hits among the entire drug combination library in a parallel and rapid manner.

168 s proteasome inhibitors and immunomodulatory drug combination maintenance therapy for this patient po

169 ter that can be used to identify problematic drug combinations matrices and prevent further analysis

170 s recently emerged that many clinical cancer drug combinations may derive their efficacy from indepen

171 Additional drug combinations may help to further delay ESKD in type

172 Therefore, this drug combination might be of therapeutic benefit in pati

173 and as such the therapeutic window for this drug combination needs to be determined.

174 Twenty (63%) reported at least 1 pathogen-drug combination not recommended for primary or suppleme

175 INTERPRETATION: The two-drug combination of all-injectable, long-acting cabotegr

176 cyclophosphamide) should be offered a three-drug combination of an NK1 receptor antagonist, a 5-HT3

177 A redeployed drug combination of bezafibrate and medroxyprogesterone

178 The redeployed drug combination of bezafibrate and medroxyprogesterone

179 ed the outcomes of VEC chemotherapy with a 5-drug combination of vincristine and carboplatin, alterna

180 -1 mRNA and virion production, we compared 2-drug combinations of leading candidate LRAs and identifi

181 Drug combinations, offering increased therapeutic effica

182 us antitumor activity than the corresponding drug combination (Olaparib + BKM120) in the MDA-MB-468 x

183 strategy, we can uncover potential effective drug combinations on a pan-cancer scale.

184 iation of current exposure to antiretroviral drug combinations on risk of cardiovascular events inclu

185 aracterize the association of antiretroviral drug combinations on risk of cardiovascular events.

186 This could be applied to drug combinations or drug repositioning, and be helpful

187 allows users to query a series of chemicals, drug combinations or multiple targets, to enable multi-d

188 A multi-drug combination, particularly including a thiazide diur

189 ng one of these associations, we validated a drug combination predicted to overcome resistance to MEK

190 sent our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergi

191 winning algorithm in the AstraZeneca-Sanger Drug Combination Prediction DREAM Challenge, which is a

192 ffective approach to examine novel drugs and drug combinations prior to animal testing.

193 Eight weeks of the drug combination produced an SVR12 in 17 of 17 (100%) pa

194 Drug combinations provide effective treatments for diver

195 herefore, optimized treatment often requires drug combinations (rather than monotherapy) and N-of-one

196 The same sequence of the three-drug combination reduced the viability of patient breast

197 ine and mycophenolate mofetil) or the triple-drug combination regimen (cyclosporine, mycophenolate mo

198 oposed program led to the development of a 3-drug combination regimen for children from scratch, inde

199 two macaques that had received the complete drug combination remained healthy and did not develop AI

200 In particular, identifying AEs caused by drug combinations remains a challenging task.

201 ver, in vivo screening of all possible multi-drug combinations remains cost-prohibitive.

202 Currently approved drug combinations result largely from empirical clinical

203 The removal of AZD4547 from the optimized drug combination resulted in 80% of cell viability inhib

204 ced when, on the basis of a matrix screen of drug combinations, ruxolitinib was combined with the Bcl

205 ma and BRAFi-resistant melanoma models, this drug combination safely and significantly extended host

206 Applying a systematic pairwise drug combination screen we observed a highly potent syne

207 ppropriate determination of the quality of a drug combination screen.

208 itor (CHK1i), we conducted a high-throughput drug combination screening in HGSOC cells.

209 cer cells, and they also show how systematic drug combination screening together with a molecular und

210 ne significance of synergy and antagonism in drug combination screens with few data point per drug pa

211 on framework for the analysis of large-scale drug combination screens.

212 The time has now come to define the optimal drug combinations, sequence of treatment, and drug regim

213 Moreover, the drug combination significantly decreases the growth of v

214 The drug combination significantly delayed the onset of epil

215 valuate and cross-compare multiple drugs and drug combinations simultaneously in living tumors and ac

216 e spatially addressable screening of optimal drug combinations simultaneously.

217 In this study, we aimed to develop drug combination strategies to further enhance the thera

218 tatistical methods to propose and validate a drug combination strategy from already approved drugs an

219 ance in the treatment of hypertension of the drug combination strategy, based on the recommendations

220 The drug combination strategy, which is significantly improv

221 Subsequent drug combination studies identified the BCL-2 inhibitor

222 Drug combination studies revealed that all five compound

223 m of 85 different cancer cell lines and 1089 drug combinations, TAIJI achieved a high prediction corr

224 1 :aa3 , as well as for the development of a drug combination targeting oxidative phosphorylation in

225 n four diseases show that network-principled drug combinations tend to have low toxicity.

226 om GABAergic drugs enables, at least for the drug combinations tested, a straightforward method to ac

227 ies of further drug screening assays and two-drug combination testing, we identified that the combina

228 understanding of drug function, and limited drug combination testing.

229 Testing a drug combination that co-targeted GLUT1 and GSH synthesi

230 However, the most effective two-drug combination that is currently available for blood-p

231 nel, we evaluated RAS pathway inhibitors and drug combinations that are currently in clinical trial f

232 First, the use of drug combinations that include bedaquiline might prevent

233 oing in our laboratory to identify effective drug combinations that include JQ1.

234 There are several promising drug combinations that may enhance the impact of STH con

235 er, this concept, can pave the way for other drug combinations that may improve the clinical applicat

236 ombinations and their translation into novel drug combinations that modulate complex human disease ph

237 es, yet the systematic discovery of gene and drug combinations that modulate these phenotypes in huma

238 Drug combinations that simultaneously suppress multiple

239 , it may be possible to rationally construct drug combinations that yield more penetrant and lasting

240 to BETi and 2) inform the rational design of drug combination therapies.

241 has implications on the choice and timing of drug combination therapies.

242 abolic reprogramming that can be targeted by drug combination therapies.

243 mize the therapeutic potential of anticancer drugs, combination therapies and multitarget drugs have

244 superior therapeutic benefits to the current drug combination therapy used in clinical practice.

245 A two-drug combination therapy where one drug targets an offen

246 applying nanocarriers to improve anticancer drug combination therapy, review the use of nanocarriers

247 improved efficacy in clinical trials of new drug combinations, thereby increasing the survival of pa

248 ams" to aid in the visualization of the many drug combinations these structures are part of.

249 led uniform drug pharmacokinetics across the drug combinations, thus allowing us to study the inheren

250 thus, we sought to develop a novel targeted drug combination to bolster its therapeutic action again

251 es provide evidence of the potential of this drug combination to eliminate FLT3/ITD(+) LSCs and reduc

252 in the clinic, the quickest way to move the drug combination to patients would be to combine these a

253 This approach will likely require effective drug combinations to achieve high levels of latency reve

254 us opening a promising approach to translate drug combinations to clinically viable treatment regimen

255 genetic screening method in identifying new drug combinations to combat acquired BRAFi resistance.

256 benefit from PI3Kalpha inhibition and design drug combinations to counteract the emergence of resista

257 d tested 10 drugs in all permutations of two-drug combinations to define synergistic combinations by

258 epair, thus paving the way toward innovative drug combinations to fight cancers.

259 pathways, with implications on how to tailor drug combinations to improve therapeutic efficacy.

260 Furthermore, they can suggest drug combinations to increase efficacy and combat acquir

261 nd explore how they may relate to successful drug combinations to overcome acquired resistance to can

262 oriness and enabling platforms for screening drug combinations to thwart resistance at the individual

263 m for preclinical testing of novel drugs and drug combinations to treat ALL.

264 tic and comprehensive method to find optimal drug combinations to use in children, ideal exposures, a

265 This drug combination transiently administered for 2 weeks du

266 Empahsis will be given to the drug combination treatment as first step of the antihype

267 iotic dosages so low that the equivalent two-drug combination treatments are ineffective.

268 mirrored the patterns of response to several drug combination treatments, suggesting that the activit

269 er, pre-exposure prophylaxis (PrEP) with the drug combination Truvada can substantially decrease HIV

270 IDA based method to predict the efficacy of drug combinations using monotherapy data from high-throu

271 biased evaluation of synergistic efficacy in drug combinations using probabilistic models such as Bli

272 chronized co-delivery of the platinum-taxane drug combination via single carrier to the same targeted

273 haracterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey.

274 nd disappeared when the otherwise successful drug combination was applied to the same NSCLC cancer im

275 Tumor inhibition with the optimally selected drug combination was further confirmed by using PC-3 tum

276 This drug combination was recently approved by the U.S. Food

277 ohort, exposure to both individual drugs and drug combinations was associated with modestly increased

278 To identify novel and effective drug combinations, we performed ex vivo sensitivity prof

279 evels at the end of 2-minute applications of drug combinations were >10% of the peak response to satu

280 The drug combinations were administered in four 3-day cycles

281 Only 9 drug combinations were assessed on >1 phase 2A endpoint

282 Second, 2-drug combinations were examined for zones of synergy, an

283 The associations for some tissue-drug combinations were remarkably strong, with genetic l

284 These optimized drug combinations were significantly more potent than mo

285 Drug combinations were tested in various therapy-resista

286 Ten novel drug combinations were validated experimentally, and sev

287 pared with those receiving oral antidiabetic-drug combinations, were estimated by means of conditiona

288 m two patients exhibited unique responses to drug combinations when cultured on the drug-eluting micr

289 opment of antibiotic resistance is to design drug combinations where the development of resistance ag

290 leads directly to the design of synergistic drug combinations, which we validate systematically by c

291 ent subpopulations, for both monotherapy and drug combinations, will be important.

292 k, we modeled ways to improve the standard 3-drug combination with the addition of new drugs.

293 dy identifies a rapid approach to assess the drug combinations with a mechanistic basis for selection

294 ased models for users to predict synergistic drug combinations with dose-response information and dru

295 Results identified several drug combinations with higher efficacy than single-dose

296 We find that it is often better to use drug combinations with matched penetration profiles, alt

297 by using an in vitro system may define novel drug combinations with significant in vivo activity.

298 ignificant increase in the use of ezetimibe, drug combinations with statins, and maximal LLT.

299 tes that we can accurately predict effective drug combinations with translational value.

300 had CNS objective response rates (ORR), the drug combination would be deemed promising.