Georgetown Lombardi Cancer Center
Georgetown Lombardi Cancer Center
PubMed | Vanderbilt Ingram Cancer Center, Moffitt Cancer Center, Roswell Park Cancer Institute, Georgetown Lombardi Cancer Center and University of Alabama at Birmingham
Type: | Journal: Journal for immunotherapy of cancer | Year: 2016
Anti-PD-1 therapy is increasingly used in various advanced malignancies. Patients with baseline organ dysfunction are largely excluded from clinical trials. Therefore it is unclear whether anti-PD-1 therapy is safe or effective in this setting. Further, these patients are often not candidates for other anti-cancer therapies, highlighting their need for active treatment options.We performed a retrospective analysis of patients from multiple centers with advanced solid tumors and baseline organ dysfunction who received anti-PD-1 therapy. Organ dysfunction was defined as cardiac (left ventricular ejection fraction 45%), renal (creatinine 2mg/dL or GFR 30ml/min) or hepatic dysfunction (evidence of cirrhosis on imaging or AST, ALT or bilirubin 3x ULN). We assessed change in organ dysfunction, immune related adverse events (irAEs), response rate, progression free survival (PFS) and overall survival (OS).We identified 27 patients eligible for inclusion with the following diseases: renal cell carcinoma (In our experience, anti-PD-1 agents in this group of patients with cardiac, hepatic or renal dysfunction were associated with tolerable irAEs and infrequent manageable worsening of organ dysfunction. Further, objective responses and prolonged PFS were observed in a number of patients. Thus, patients with baseline organ dysfunction may be considered for anti-PD-1 therapy with appropriate clinical monitoring.
Issa N.T.,Georgetown Lombardi Cancer Center |
Peters O.J.,Georgetown Lombardi Cancer Center |
Byers S.W.,Georgetown Lombardi Cancer Center |
Byers S.W.,Georgetown University |
And 2 more authors.
Combinatorial Chemistry and High Throughput Screening | Year: 2015
We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing. © 2015 Bentham Science Publishers.
PubMed | Georgetown Lombardi Cancer Center
Type: Journal Article | Journal: Current topics in medicinal chemistry | Year: 2013
The process of discovering a pharmacological compound that elicits a desired clinical effect with minimal side effects is a challenge. Prior to the advent of high-performance computing and large-scale screening technologies, drug discovery was largely a serendipitous endeavor, as in the case of thalidomide for erythema nodosum leprosum or cancer drugs in general derived from flora located in far-reaching geographic locations. More recently, de novo drug discovery has become a more rationalized process where drug-target-effect hypotheses are formulated on the basis of already known compounds/protein targets and their structures. Although this approach is hypothesis-driven, the actual success has been very low, contributing to the soaring costs of research and development as well as the diminished pharmaceutical pipeline in the United States. In this review, we discuss the evolution in computational pharmacology as the next generation of successful drug discovery and implementation in the clinic where high-performance computing (HPC) is used to generate and validate drug-target-effect hypotheses completely in silico. The use of HPC would decrease development time and errors while increasing productivity prior to in vitro, animal and human testing. We highlight approaches in chemoinformatics, bioinformatics as well as network biopharmacology to illustrate potential avenues from which to design clinically efficacious drugs. We further discuss the implications of combining these approaches into an integrative methodology for high-accuracy computational predictions within the context of drug repositioning for the efficient streamlining of currently approved drugs back into clinical trials for possible new indications.