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.

The evidence base of US Food and Drug Administration approvals of novel cancer therapies from 2000 to 2020.

Concerns have been raised that regulatory programs to accelerate approval of cancer drugs in cancer may increase uncertainty about benefits and harms for survival and quality of life (QoL). We analyzed all pivotal clinical trials and all non-pivotal randomized controlled trials (RCTs) for all cancer drugs approved for the first time by the FDA between 2000 and 2020. We report regulatory and trial characteristics. Effects on overall survival (OS), progression-free survival and tumor response were summarized in meta-analyses. Effects on QoL were qualitatively summarized. Between 2000 and 2020, the FDA approved 145 novel cancer drugs for 156 indications based on 190 clinical trials. Half of indications (49%) were approved without RCT evidence; 82% had a single clinical trial only. OS was primary endpoint in 14% of trials and QoL data were available from 25%. The median OS benefit was 2.55 months (IQR, 1.33-4.28) with a mean hazard ratio for OS of 0.75 (95%CI, 0.72-0.79, I2  = 42). Improvement for QoL was reported for 7 (4%) of 156 indications. Over time, priority review was used increasingly and the mean number of trials per indication decreased from 1.45 to 1.12. More trials reported results on QoL (19% in 2000-2005; 41% in 2016-2020). For 21 years, novel cancer drugs have typically been approved based on one single, often uncontrolled, clinical trial, measuring surrogate endpoints. This leaves cancer patients without solid evidence that novel drugs improve their survival or QoL and there is no indication towards improvement.

Characteristics and survival of patients with cancer with intended off-label use-a cohort study.

OBJECTIVE: To describe the characteristics and the survival of patients with cancer with intended off-label use (OLU) cancer treatment and reimbursement request. DESIGN: Cohort study using medical record data. SETTING: Three major cancer centres in Switzerland. PARTICIPANTS: 519 patients with cancer and a reimbursement request for OLU between January 2015 and July 2018. MAIN OUTCOMES: Characteristics of patients with cancer with and without access to intended OLU. Characteristics included the Glasgow prognostic score (GPS) which includes C reactive protein and albumin and discriminates prognostic groups. RESULTS: OLU was intended for 519 (17%) of 3046 patients with cancer, as first-line treatment in 51% (n=264) and second-line in 31% (n=162). Of the 519 patients, 63% (n=328) were male, 63% (n=329) had solid cancer and 21% (n=111) had a haematological malignancy. Their median overall survival was 23.6 months (95% CI: 19.0 to 32.5). Access to OLU had 389 (75%) patients who were compared with patients without access on average 4.9 years younger (mean; 95% CI: 1.9 to 7.9 years), had a better overall prognosis according to the GPS (51% with GPS of 0 vs 39%; OR: 1.62 (95% CI: 1.01 to 2.59)), had less frequently solid cancer (62% vs 71%; OR: 0.66 (95% CI: 0.41 to 1.05)) and advanced stage cancer (53% vs 70%; OR: 0.48 (95% CI: 0.30 to 0.75)), were more frequently treatment-naive (53% vs 43%; OR: 1.55 (95% CI 1.01 to 2.39)) and were more frequently in an adjuvant/neoadjuvant treatment setting (14% vs 5%; OR: 3.39 (95% CI: 1.45 to 9.93)). Patients with access to OLU had a median OS of 31.1 months versus 8.7 months for patients without access (unadjusted HR: 0.54; (95% CI: 0.41 to 0.70)). CONCLUSION: Contrary to the common assumption, OLU in oncology is typically not primarily intended for patients with exhausted treatment options. Patient characteristics largely differ between patients with and without access to intended OLU. More systematic evaluations of the benefits and harms of OLU in cancer care and the regulation of its access is warranted.

Association of Supporting Trial Evidence and Reimbursement for Off-Label Use of Cancer Drugs.

Importance: In many health systems, access to off-label drug use is controlled through reimbursement restrictions by health insurers, especially for expensive cancer drugs. Objective: To determine whether evidence from randomized clinical trials is associated with reimbursement decisions for requested off-label use of anticancer drugs in the Swiss health system. Design, Setting, and Participants: This cross-sectional study used reimbursement requests from routinely collected health records of 5809 patients with drug treatment for cancer between January 2015 and July 2018 in 3 major cancer centers, covering cancer care of approximately 5% of the Swiss population, to identify off-label drug use. For each off-label use indication with 3 or more requests, randomized clinical trial evidence on treatment benefits was systematically identified for overall survival (OS) or progression-free survival (PFS). Data were analyzed from August 2018 to December 2020. Exposures: Available randomized clinical trial evidence on benefits for OS or PFS for requested off-label use indications. Main Outcomes and Measures: The main outcome was the association between evidence for treatment benefit (expressed as improved OS or PFS) and reimbursement in multivariable regression models. Results: Among 3046 patients with cancer, 695 off-label use reimbursement requests in 303 different indications were made for 598 patients (median [interquartile range] age, 64 [53-73] years; 420 [60%] men). Off-label use was intended as first-line treatment in 311 requests (45%). Reimbursement was accepted in 446 requests (64%). For 71 indications, including 431 requests for 376 patients, there were 3 or more requests. Of these, 246 requests (57%) had no supporting evidence for OS or PFS benefit. Reimbursement was granted in 162 of 246 requests without supporting evidence (66%). Of 117 requests supported by OS benefit, 79 (67%) were reimbursed, and of 68 requests supported by PFS benefit alone, 54 (79%) were reimbursed. Evidence of OS benefit from randomized clinical trials was not associated with a higher chance of reimbursement (odds ratio, 0.76, 95% CI, 0.45-1.27). Conclusions and Relevance: These findings suggest that in a health care system enabling access to off-label use, it was frequently intended as a first-line treatment in cancer care. Availability of randomized clinical trial evidence showing survival benefit was not associated with reimbursement decisions for off-label anticancer drug treatment in Switzerland. A transparent process with criteria considering clinical evidence is needed for evidence-based reimbursement decisions to ensure fair access to cancer treatments.

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.

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.

Contrasting evidence to reimbursement reality for off-label use (OLU) of drug treatments in cancer care: rationale and design of the CEIT-OLU project.

Background: Off-label use (OLU) of a drug reflects a perceived unmet medical need, which is common in oncology. Cancer drugs are often highly expensive and their reimbursement is a challenge for many healthcare systems. OLU is frequently regulated by reimbursement restrictions. For evidence-based healthcare, treatment ought to be reimbursed if there is sufficient clinical evidence for treatment benefit independently of patient factors not related to the treatment indication. However, little is known about the reality of OLU reimbursement and its association with the underlying clinical evidence. Here, we aim to investigate the relationship of reimbursement decisions with the underlying clinical evidence. Methods/ design: We will extract patient characteristics and details on treatment and reimbursement of cancer drugs from over 3000 patients treated in three Swiss hospitals. We will systematically search for clinical trial evidence on benefits associated with OLU in the most common indications. We will describe the prevalence of OLU in Switzerland and its reimbursement in cancer care, and use multivariable logistic regression techniques to investigate the association of approval/rejection of a reimbursement requests to the evidence on treatment effects and to further factors, including type of drug, molecular predictive markers and the health insurer. Discussion: Our study will provide a systematic overview and assessment of OLU and its reimbursement reality in Switzerland. We may provide a better understanding of the access to cancer care that is regulated by health insurers and we hope to identify factors that determine the level of evidence-based cancer care in a highly diverse western healthcare system.

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 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.