Rituximab in primary central nervous system lymphoma-A systematic review and meta-analysis.

The CD-20 antibody rituximab is a standard component of treatment of non-Hodgkin B-cell lymphomas, including diffuse large B-cell lymphoma (DLBCL). Primary DLBCL of the central nervous system, also called primary central nervous system lymphoma (PCNSL), is a DLBCL confined to the central nervous system. There has been debate whether intravenous rituximab accumulates sufficiently in the central nervous system to exert an effect. In this systematic review, we assess the benefits and harms of rituximab in the treatment of immunocompetent patients with PCNSL. By searching MEDLINE, CENTRAL, and ClincialTrials.gov up to March 2019, we identified randomized controlled trials (RCTs) investigating the effect of rituximab in patients with PCNSL. We extracted study characteristics and results, assessed risk of bias, performed trial-level random-effects meta-analyses, and graded the certainty of evidence. The protocol was registered with PROSPERO (CRD42019121965). Main outcomes were overall survival (time to death), progression-free survival (time to progression or death), quality of life, grades 3 and 4 toxicity, and treatment-related mortality. We included two RCTs with a total of 343 participants. Overall survival was not statistically significantly improved (HR 0.76; 95% CI, 0.52-1.12; low certainty), with 187 fewer to 39 more deaths after 2 years in 1000 treated patients. Low certainty of evidence indicated that rituximab improved progression-free survival (HR 0.65; 95% CI, 0.45-0.95), which translated into 137 fewer progressions or deaths after 2 years in 1000 treated patients (231 to 18 fewer). None of the RCTs provided data on quality of life. We found no evidence that rituximab increased grades 3 and 4 toxicity or treatment-related mortality (RR 0.53; 95% CI, 0.20-1.37; low certainty). Overall, the available evidence suggests with low certainty that rituximab in combination with methotrexate-based chemotherapy may improve progression-free survival in immunocompetent patients with newly diagnosed PCNSL, the pooled effect estimates did not show evidence for improvement of overall survival.

Predicting protein targets for drug-like compounds using transcriptomics.

An expanded chemical space is essential for improved identification of small molecules for emerging therapeutic targets. However, the identification of targets for novel compounds is biased towards the synthesis of known scaffolds that bind familiar protein families, limiting the exploration of chemical space. To change this paradigm, we validated a new pipeline that identifies small molecule-protein interactions and works even for compounds lacking similarity to known drugs. Based on differential mRNA profiles in multiple cell types exposed to drugs and in which gene knockdowns (KD) were conducted, we showed that drugs induce gene regulatory networks that correlate with those produced after silencing protein-coding genes. Next, we applied supervised machine learning to exploit drug-KD signature correlations and enriched our predictions using an orthogonal structure-based screen. As a proof-of-principle for this regimen, top-10/top-100 target prediction accuracies of 26% and 41%, respectively, were achieved on a validation of set 152 FDA-approved drugs and 3104 potential targets. We then predicted targets for 1680 compounds and validated chemical interactors with four targets that have proven difficult to chemically modulate, including non-covalent inhibitors of HRAS and KRAS. Importantly, drug-target interactions manifest as gene expression correlations between drug treatment and both target gene KD and KD of genes that act up- or down-stream of the target, even for relatively weak binders. These correlations provide new insights on the cellular response of disrupting protein interactions and highlight the complex genetic phenotypes of drug treatment. With further refinement, our pipeline may accelerate the identification and development of novel chemical classes by screening compound-target interactions.

Modulation of the substrate specificity of the kinase PDK1 by distinct conformations of the full-length protein.

The activation of at least 23 different mammalian kinases requires the phosphorylation of their hydrophobic motifs by the kinase PDK1. A linker connects the phosphoinositide-binding PH domain to the catalytic domain, which contains a docking site for substrates called the PIF pocket. Here, we used a chemical biology approach to show that PDK1 existed in equilibrium between at least three distinct conformations with differing substrate specificities. The inositol polyphosphate derivative HYG8 bound to the PH domain and disrupted PDK1 dimerization by stabilizing a monomeric conformation in which the PH domain associated with the catalytic domain and the PIF pocket was accessible. In the absence of lipids, HYG8 potently inhibited the phosphorylation of Akt (also termed PKB) but did not affect the intrinsic activity of PDK1 or the phosphorylation of SGK, which requires docking to the PIF pocket. In contrast, the small-molecule valsartan bound to the PIF pocket and stabilized a second distinct monomeric conformation. Our study reveals dynamic conformations of full-length PDK1 in which the location of the linker and the PH domain relative to the catalytic domain determines the selective phosphorylation of PDK1 substrates. The study further suggests new approaches for the design of drugs to selectively modulate signaling downstream of PDK1.

Characteristics and interpretation of subgroup analyses based on tumour characteristics in randomised trials testing target-specific anticancer drugs: design of a systematic survey.

BACKGROUND: Target-specific anticancer drugs are under rapid development. Little is known, however, about the risk of administering target-specific drugs to patients who have tumours with molecular alterations or other characteristics that can make the drug ineffective or even harmful. An increasing number of randomised clinical trials (RCTs) investigating target-specific anticancer drugs include subgroup analyses based on tumour characteristics. Such subgroup analyses have the potential to be more credible and influential than subgroup analyses based on traditional factors such as sex or tumour stage. In addition, they may more frequently lead to qualitative subgroup effects, that is, show benefit in one but harm in another subgroup of patients (eg, if the tumour characteristic makes the drug ineffective or even enhance tumour growth). If so, subgroup analyses based on tumour characteristics would be highly relevant for patient safety. The aim of this study is to systematically assess the frequency and characteristics of subgroup analyses based on tumour characteristics, the frequency of qualitative subgroup effects, their credibility, and the interpretations that investigators and guidelines developers report. METHODS AND ANALYSIS: We will perform a systematic survey of 433 RCTs testing the effect of target-specific anticancer drugs. Teams of methodologically trained investigators and oncologists will identify eligible studies, extract relevant data and assess the credibility of putative subgroup effects using a recently developed formal instrument. We will systematically assess how trial investigators interpret apparent subgroup effects based on tumour characteristics and the extent to which they influence subsequent practice guidelines. Our results will provide empirical data characterising an increasingly used type of subgroup analysis in cancer trials and its potential impact on precision medicine to predict benefit or harm. ETHICS AND DISSEMINATION: Formal ethical approval is not required for this study. We will disseminate the findings in a peer-reviewed and open-access journal publication.

Clinical Trial Evidence Supporting US Food and Drug Administration Approval of Novel Cancer Therapies Between 2000 and 2016.

Importance: Clinical trial evidence used to support drug approval is typically the only information on benefits and harms that patients and clinicians can use for decision-making when novel cancer therapies become available. Various evaluations have raised concern about the uncertainty surrounding these data, and a systematic investigation of the available information on treatment outcomes for cancer drugs approved by the US Food and Drug Administration (FDA) is warranted. Objective: To describe the clinical trial data available on treatment outcomes at the time of FDA approval of all novel cancer drugs approved for the first time between 2000 and 2016. Design, Setting, and Participants: This comparative effectiveness study analyzed randomized clinical trials and single-arm clinical trials of novel drugs approved for the first time to treat any type of cancer. Approval packages were obtained from drugs@FDA, a publicly available database containing information on drug and biologic products approved for human use in the US. Data from January 2000 to December 2016 were included in this study. Main Outcomes and Measures: Regulatory and clinical trial characteristics were described. For randomized clinical trials, summary treatment outcomes for overall survival, progression-free survival, and tumor response across all therapies were calculated, and median absolute survival increases were estimated. Tumor types and regulatory characteristics were assessed separately. Results: Between 2000 and 2016, 92 novel cancer drugs were approved by the FDA for 100 indications based on data from 127 clinical trials. The 127 clinical trials included a median of 191 participants (interquartile range [IQR], 106-448 participants). Overall, 65 clinical trials (51.2%) were randomized, and 95 clinical trials (74.8%) were open label. Of 100 indications, 44 indications underwent accelerated approval, 42 indications were for hematological cancers, and 58 indications were for solid tumors. Novel drugs had mean hazard ratios of 0.77 (95% CI, 0.73-0.81; I2 = 46%) for overall survival and 0.52 (95% CI, 0.47-0.57; I2 = 88%) for progression-free survival. The median tumor response, expressed as relative risk, was 2.37 (95% CI, 2.00-2.80; I2 = 91%). The median absolute survival benefit was 2.40 months (IQR, 1.25-3.89 months). Conclusions and Relevance: In this study, data available at the time of FDA drug approval indicated that novel cancer therapies were associated with substantial tumor responses but with prolonging median overall survival by only 2.40 months. Approval data from 17 years of clinical trials suggested that patients and clinicians typically had limited information available regarding the benefits of novel cancer treatments at market entry.

Prediction of RECRUITment In randomized clinical Trials (RECRUIT-IT)-rationale and design for an international collaborative study.

BACKGROUND: Poor recruitment of patients is the predominant reason for early termination of randomized clinical trials (RCTs). Systematic empirical investigations and validation studies of existing recruitment models, however, are lacking. We aim to provide evidence-based guidance on how to predict and monitor recruitment of patients into RCTs. Our specific objectives are the following: (1) to establish a large sample of RCTs (target n = 300) with individual patient recruitment data from a large variety of RCTs, (2) to investigate participant recruitment patterns and study site recruitment patterns and their association with the overall recruitment process, (3) to investigate the validity of a freely available recruitment model, and (4) to develop a user-friendly tool to assist trial investigators in the planning and monitoring of the recruitment process. METHODS: Eligible RCTs need to have completed the recruitment process, used a parallel group design, and investigated any healthcare intervention where participants had the free choice to participate. To establish the planned sample of RCTs, we will use our contacts to national and international RCT networks, clinical trial units, and individual trial investigators. From included RCTs, we will collect patient-level information (date of randomization), site-level information (date of trial site activation), and trial-level information (target sample size). We will examine recruitment patterns using recruitment trajectories and stratifications by RCT characteristics. We will investigate associations of early recruitment patterns with overall recruitment by correlation and multivariable regression. To examine the validity of a freely available Bayesian prediction model, we will compare model predictions to collected empirical data of included RCTs. Finally, we will user-test any promising tool using qualitative methods for further tool improvement. DISCUSSION: This research will contribute to a better understanding of participant recruitment to RCTs, which could enhance efficiency and reduce the waste of resources in clinical research with a comprehensive, concerted, international effort.

Modulation of the Allosteric Communication between the Polo-Box Domain and the Catalytic Domain in Plk1 by Small Compounds.

The Polo-like kinases (Plks) are an evolutionary conserved family of Ser/Thr protein kinases that possess, in addition to the classical kinase domain at the N-terminus, a C-terminal polo-box domain (PBD) that binds to phosphorylated proteins and modulates the kinase activity and its localization. Plk1, which regulates the formation of the mitotic spindle, has emerged as a validated drug target for the treatment of cancer, because it is required for numerous types of cancer cells but not for the cell division in noncancer cells. Here, we employed chemical biology methods to investigate the allosteric communication between the PBD and the catalytic domain of Plk1. We identified small compounds that bind to the catalytic domain and inhibit or enhance the interaction of Plk1 with the phosphorylated peptide PoloBoxtide in vitro. In cells, two new allosteric Plk1 inhibitors affected the proliferation of cancer cells in culture and the cell cycle but had distinct phenotypic effects on spindle formation. Both compounds inhibited Plk1 signaling, indicating that they specifically act on Plk1 in cultured cells.

Bidirectional Allosteric Communication between the ATP-Binding Site and the Regulatory PIF Pocket in PDK1 Protein Kinase.

Allostery is a phenomenon observed in many proteins where binding of a macromolecular partner or a small-molecule ligand at one location leads to specific perturbations at a site not in direct contact with the region where the binding occurs. The list of proteins under allosteric regulation includes AGC protein kinases. AGC kinases have a conserved allosteric site, the phosphoinositide-dependent protein kinase 1 (PDK1)-interacting fragment (PIF) pocket, which regulates protein ATP-binding, activity, and interaction with substrates. In this study, we identify small molecules that bind to the ATP-binding site and affect the PIF pocket of AGC kinase family members, PDK1 and Aurora kinase. We describe the mechanistic details and show that although PDK1 and Aurora kinase inhibitors bind to the conserved ATP-binding site, they differentially modulate physiological interactions at the PIF-pocket site. Our work outlines a strategy for developing bidirectional small-molecule allosteric modulators of protein kinases and other signaling proteins.