Cancer drug development: The missing links

Although better science and technology has been linked with better health care, however, reality is much different. Although America and most of Europe are equipped with most advanced science and technology, paradoxically cancer incidence is highest in the world. This indicates that science and technology alone is not sufficient in treating diseases like cancer. It is also now well recognized that more than 95% of the drugs/compounds that kill either cancer cells in culture or regress the tumors in animals, fail in phase I clinical trials in humans, indicating that most pre-clinical models of cancer are inadequate. In addition, most of the anticancer drugs that are approved by the regulatory agencies such as FDA either has no effect on the overall survival of the cancer patient or may provide an increase in few months in overall survival. This is despite the fact that most targeted therapies that are currently available are highly expensive; thus suggesting the lack of affordability. This review is meant to focus on some of these problems in detail and then provide potential solutions since most cancers are caused by multiple genes, and thus multi-targeted therapies are needed such as natural products which are inexpensive, safe and have been used for thousands of years for both prevention and treatment of cancer. Impact statement The success rate for cancer drugs which enter into phase 1 clinical trials is utterly less. Why the vast majority of drugs fail is not understood but suggests that pre-clinical studies are not adequate for human diseases. In 1975, as per the Tufts Center for the Study of Drug Development, pharmaceutical industries expended 100 million dollars for research and development of the average FDA approved drug. By 2005, this figure had more than quadrupled, to $1.3 billion. In order to recover their high and risky investment cost, pharmaceutical companies charge more for their products. However, there exists no correlation between drug development cost and actual sale of the drug. This high drug development cost could be due to the reason that all patients might not respond to the drug. Hence, a given drug has to be tested in large number of patients to show drug benefits and obtain significant results.

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Prediction of Drug Approval After Phase I Clinical Trials in Oncology: RESOLVED2

JCO Clinical Cancer Informatics ◽

10.1200/cci.19.00023 ◽

2019 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Guillaume Beinse ◽

Virgile Tellier ◽

Valentin Charvet ◽

Eric Deutsch ◽

Isabelle Borget ◽

...

Keyword(s):

Machine Learning ◽

Clinical Trials ◽

Drug Development ◽

Phase I ◽

Attrition Rate ◽

Phase I Clinical Trials ◽

Test Set ◽

Free Survival ◽

Fda Approval ◽

Independent Test

PURPOSE Drug development in oncology currently is facing a conjunction of an increasing number of antineoplastic agents (ANAs) candidate for phase I clinical trials (P1CTs) and an important attrition rate for final approval. We aimed to develop a machine learning algorithm (RESOLVED2) to predict drug development outcome, which could support early go/no-go decisions after P1CTs by better selection of drugs suitable for further development. METHODS PubMed abstracts of P1CTs reporting on ANAs were used together with pharmacologic data from the DrugBank5.0 database to model time to US Food and Drug Administration (FDA) approval (FDA approval-free survival) since the first P1CT publication. The RESOLVED2 model was trained with machine learning methods. Its performance was evaluated on an independent test set with weighted concordance index (IPCW). RESULTS We identified 462 ANAs from PubMed that matched with DrugBank5.0 (P1CT publication dates 1972 to 2017). Among 1,411 variables, 28 were used by RESOLVED2 to model the FDA approval-free survival, with an IPCW of 0.89 on the independent test set. RESOLVED2 outperformed a model that was based on efficacy/toxicity (IPCW, 0.69). In the test set at 6 years of follow-up, 73% (95% CI, 49% to 86%) of drugs predicted to be approved were approved, whereas 92% (95% CI, 87% to 98%) of drugs predicted to be nonapproved were still not approved (log-rank P < .001). A predicted approved drug was 16 times more likely to be approved than a predicted nonapproved drug (hazard ratio, 16.4; 95% CI, 8.40 to 32.2). CONCLUSION As soon as P1CT completion, RESOLVED2 can predict accurately the time to FDA approval. We provide the proof of concept that drug development outcome can be predicted by machine learning strategies.

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Sequential Treatment With Bendamustine, Rituximab, Chlorambucil and Fludarabine Leads To a Major Survival Improvement Of Patients With Chronic Lymphocytic Leukemia (CLL) In Routine Care

Blood ◽

10.1182/blood.v122.21.5613.5613 ◽

2013 ◽

Vol 122 (21) ◽

pp. 5613-5613

Author(s):

Rudolf Weide ◽

Stefan Feiten ◽

Vera Friesenhahn ◽

Jochen Heymanns ◽

Kristina Kleboth ◽

...

Keyword(s):

Overall Survival ◽

Clinical Trials ◽

Median Overall Survival ◽

Conflicts Of Interest ◽

Relative Survival ◽

Routine Care ◽

Initial Diagnosis ◽

Lymphocytic Leukemia ◽

Number Of Patients ◽

Binet Stage

Abstract Introduction Progress has been made in diagnosis and treatment of patients with CLL who receive their treatment within prospective clinical trials. Due to necessary inclusion and exclusion criteria only a very limited number of patients are treated in studies. Therefore results from clinical trials can't be transferred into routine care. No clinical practice data are available how patients with CLL are diagnosed and treated in routine care and whether improvements in survival are achieved. Methods A retrospective analysis of all patients with CLL who were treated in an oncology group practice in Germany between 1995-2012. Relevant clinical data concerning diagnosis, treatment and survival were transferred from clinical files into a database and analyzed statistically using SPSS and SURVSOFT. Results 580 CLL patients with a median age of 67 (35-92) were identified. At initial diagnosis 446 patients (76.9 %) were in Binet stage A, 69 (11.9%) Binet stage B and 31 (5.3%) Binet stage C. Due to external diagnosis of 34 patients (5.9%) the stage at initial diagnosis couldn't be retrieved. 323 patients (55.7 %) never received any treatment. 257 patients (44.3%) needed therapy with a median of 2 therapy lines (1-11). Regimens most frequently applied were: Bendamustine-containig (66.9%), Rituximab-containing (62.3%), Chlorambucil-containing (61.5%), Bendamustine+Rituximab-combinations (48.2%) and Fludarabine-containing (40.9%). 21.0% of patients were treated within a clinical trial. 5 and 10 year absolute overall survival was 83.6% and 60.9%. Relative survival after 5 and 10 years was 96.1% and 82.3%. Median overall survival according to Binet stage was 16 years for Binet A, 9 years for Binet B and 8 years for Binet C. Median relative survival was 20.8 years for Binet A, 14.0 years for Binet B and 8.6 years for Binet C. Patients who needed therapy had a median overall survival of 11 years (0-41) compared to 18 years (0-23+) of patients who never needed any therapy. Conclusions 55.7% of CLL-patients never needed any therapy. Patients who needed therapy had a much lower life expectancy compared to patients who never needed therapy. Treatment consisted mainly of Bendamustine, Rituximab, Chlorambucil, Bendamustine+Rituximab-combinations and Fludarabine leading to a marked prolongation of survival compared to historical controls and registry data. Disclosures: No relevant conflicts of interest to declare.

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Characteristics and outcomes of patients with gallbladder cancer and cholangiocarcinoma referred to a phase I clinic.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.4_suppl.364 ◽

2012 ◽

Vol 30 (4_suppl) ◽

pp. 364-364 ◽

Cited By ~ 1

Author(s):

Ishwaria Mohan Subbiah ◽

Vivek Subbiah ◽

Ahmed Omar Kaseb ◽

Filip Janku ◽

Jennifer J. Wheler ◽

...

Keyword(s):

Overall Survival ◽

Clinical Trials ◽

Phase I ◽

Median Time ◽

Median Survival ◽

Phase I Trial ◽

Mutational Analysis ◽

Phase 1 ◽

Cancer Center ◽

Phase I Clinical Trials

364 Background: The prognosis of cholangiocarcinoma (CC) and gallbladder carcinoma (GC) remains grim. The purpose of this study was to report the presenting characteristics and outcomes of patients with CC and GC treated on phase 1 clinical trials focused on targeted agents at a major cancer center. Methods: We reviewed the records of consecutive patients with GC and CC in the Phase I Clinical Trials Program at the M. D. Anderson Cancer Center from Nov 2004. We assessed the relationship between overall survival, patients' tumor types, and mutations, demographic and clinical characteristics. Results: Fifty-two patients were identified (7 with GC, 45 with CC). The median age was 58 yrs (range, 20-75 yrs). ECOG performance status (PS) was 0, 1, 2, and 3 in 9 (17%), 30 (58%), 7 (13%), and 6 (12%) pts, respectively. Median number of prior therapies was 3 (range 0-17). The median time from diagnosis of metastatic disease to primary Phase I clinic evaluation was 14.6 months. Of 52 patients, 17 (33%) were not enrolled on a Phase I trial due to decline in PS (n=13) or decision to pursue other treatments (n=4). Of 35 patients evaluable for response, 2 (6%) had a partial response (PR), and 3 (9%) had stable disease > 4 months. Prognostic factors analyzed include Hg < 10.5 g/dL, elevated CA 19-9 (>47 ng/mL), ECOG PS > 3, LDH > 618 IU/L, albumin < 3.5 g/dL, platelets < 150 K/UL, and number of metastatic sites. Full analysis including the mutational analysis for PIK3CA, KRAS, BRAF, TP53 is in progress. Median survival since presentation to the Phase I clinic was 4.1 months (range 2.3 - 30.8 months). Median overall survival from diagnosis was 23.9 months. The median survival since enrollment in a Phase I trial was 4.6 months w the median time to disease progression on Phase I treatment was 2.2 months (range 0.6 - 25.6 months). Conclusions: Prognosis of pts with CC and GC referred for phase I studies remains poor. Further analysis including complete mutational profiles of CC and GC patients will be reported.

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Role of Isolated Limb Perfusion in the Era of Targeted Therapies and Immunotherapy in Melanoma. A Systematic Review of The Literature

Cancers ◽

10.3390/cancers13215485 ◽

2021 ◽

Vol 13 (21) ◽

pp. 5485

Author(s):

Lourdes Sevilla-Ortega ◽

Lara Ferrándiz-Pulido ◽

Natalia Palazón-Carrión ◽

María del Carmen Álamo de la Gala ◽

Rubén de Toro-Salas ◽

...

Keyword(s):

Systematic Review ◽

Overall Survival ◽

Clinical Trials ◽

Targeted Therapies ◽

Response Rate ◽

Isolated Limb Perfusion ◽

Cochrane Library ◽

Safe Procedure ◽

Review Of The Literature ◽

Limb Perfusion

Background. Isolated limb perfusion (ILP) is a locoregional procedure indicated by the unresectable melanoma of the limbs. Its complexity and highly demanding multidisciplinary approach means that it is a technique only implemented in a few referral centers around the globe. This report aims to examine its potential role in the era of targeted therapies and immunotherapy by conducting a systematic review of the literature on ILP. Methods. PubMed, Embase and Cochrane Library were searched. The eligibility criteria included publications from 2000–2020 providing valid data o effectiveness, survival or toxicity. Studies in which the perfusion methodology was not clearly described, letters to the editor, non-systematic reviews and studies that applied outdated clinical guidelines were excluded. To rule out studies of a low methodological quality and assess the risk of bias, the following aspects were also required: a detailed description of the applied ILP regimen, the clinical context, follow-up periods, analyzed clinical endpoints, and the number of analyzed ILPs. The disagreements were resolved by consensus. The results are presented in tables and figures. Results. Twenty-seven studies including 2637 ILPs were selected. The median overall response rate was 85%, with a median complete response rate of 58.5%. The median overall survival was 38 months, with a 5-year overall survival of 35%. The toxicity was generally mild according to Wieberdink toxicity criteria. Discussion. ILP still offer a high efficacy in selected patients. The main limitation of our review is the heterogeneity and age of most of the articles, as well as the absence of clinical trials comparing ILP with other procedures, making it difficult to transfer its results to the current era. Conclusions. ILP is still an effective and safe procedure for selected patients with unresectable melanoma of the limbs. In the era of targeted therapies and immunotherapy, ILP remains an acceptable and reasonable palliative treatment alternative, especially to avoid limb amputations. The ongoing clinical trials combining systemic therapies and ILP will provide more valuable information in the future to clarify the potential synergism of both strategies.

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A dynamic stopping rule for phase I clinical trials

Biometrical Letters ◽

10.2478/bile-2018-0002 ◽

2018 ◽

Vol 55 (1) ◽

pp. 17-30 ◽

Cited By ~ 1

Author(s):

M. Iftakhar Alam ◽

Mohaimen Mansur

Keyword(s):

Clinical Trials ◽

Phase I ◽

Dose Response ◽

Parameter Estimate ◽

Maximum Tolerated Dose ◽

Stopping Rule ◽

Bayesian Posterior Probability ◽

Phase I Clinical Trials ◽

Response Data ◽

Number Of Patients

Summary This paper investigates a stopping rule to be utilised in phase I clinical trials. The motivation is to develop a dynamic rule so that a trial stops early if the maximum tolerated dose lies towards the beginning of a dose region. Also, it will employ many patients if the maximum tolerated dose lies towards the end of a dose region. A two-parameter logistic model is assumed for the dose-response data. A trial is stopped early before reaching the maximum number of patients when the width of the Bayesian posterior probability interval of the slope parameter meets a desired value. Instead of setting a pre-specified width to stop at, we determine it based on the parameter estimate obtained after a reasonable number of steps in a trial. Simulation studies of six plausible dose-response scenarios show that the proposed stopping rule is capable of limiting the number of patients to be recruited depending on the underlying scenario. Although the rule is applied to a D-optimum design here, it will be equally applicable to other model-based designs.

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Model-Assisted Designs for Early-Phase Clinical Trials: Simplicity Meets Superiority

JCO Precision Oncology ◽

10.1200/po.19.00032 ◽

2019 ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Ying Yuan ◽

J. Jack Lee ◽

Susan G. Hilsenbeck

Keyword(s):

Clinical Trials ◽

Drug Development ◽

Late Onset ◽

Single Agent ◽

Adaptive Designs ◽

Superior Performance ◽

Delayed Response ◽

Phase I Clinical Trials ◽

Phase Ii Trials ◽

Major Barrier

Drug development enterprise is struggling because of prohibitively high costs and slow progress. There is urgent need for adoption of novel adaptive designs to improve the efficiency and success of clinical trials. A major barrier is that many conventional designs are inadequate for modern drug development, yet most novel adaptive designs are difficult to understand, require complicated statistical modeling, demand complex computation, and need expensive infrastructure for implementation. The objective of this article is to introduce and review a class of novel adaptive designs, known as model-assisted designs, to remove this barrier and increase the use of novel adaptive designs. Model-assisted designs enjoy superior performance comparable to more complicated, model-based adaptive designs, but their decision rule can be pretabulated and included in the protocol—thus implemented as simply as the conventional designs. We review state-of-the-art model-assisted designs for phase I clinical trials for single-agent, drug-combination and late-onset toxicity scenarios. We also briefly introduce model-assisted designs for phase II trials to handle binary, coprimary endpoints and delayed response. Freely available user-friendly software and trial examples (trialdesign.org) facilitate the adoption of model-assisted designs.

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The Michael J. Fox Foundation’s Strategies for Accelerating Translation of LRRK2 into Therapies for Parkinson Disease

Cells ◽

10.3390/cells9081878 ◽

2020 ◽

Vol 9 (8) ◽

pp. 1878

Author(s):

Shalini Padmanabhan ◽

Brian K. Fiske ◽

Marco A.S. Baptista

Keyword(s):

Clinical Trials ◽

Drug Development ◽

Parkinson Disease ◽

Targeted Therapies ◽

Kinase Inhibitors ◽

Critical Research ◽

Development Activity ◽

Potential Safety ◽

Lrrk2 Kinase ◽

Holistic Description

Since 2005, The Michael J. Fox Foundation for Parkinson’s Research (MJFF) has invested significant funding and non-funding effort to accelerate research and drug development activity around the Parkinson disease (PD)-associated protein LRRK2. MJFF has spearheaded multiple public/private pre-competitive collaborations that have contributed to our understanding of LRRK2 function; de-risked potential safety questions around the therapeutic use of LRRK2 kinase inhibitors; and generated critical research tools, biosamples, and data for the field. Several LRRK2-targeted therapies are now in human testing due to the hard work of so many in the PD community. In this perspective, we present a holistic description and model of how our Foundation’s support targeted important barriers to LRRK2 research and helped move the field into clinical trials.

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Phase I clinical trials in patients with advanced non-small cell lung cancer treated within a Drug Development Unit: What have we learnt?

Lung Cancer ◽

10.1016/j.lungcan.2017.06.005 ◽

2017 ◽

Vol 111 ◽

pp. 6-11 ◽

Cited By ~ 2

Author(s):

Marta Capelan ◽

Desamparados Roda ◽

Elena Geuna ◽

Karim Rihawi ◽

Shankar Bodla ◽

...

Keyword(s):

Lung Cancer ◽

Clinical Trials ◽

Drug Development ◽

Phase I ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Phase I Clinical Trials ◽

Small Cell Lung ◽

Development Unit

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Number of Patients per Cohort and Sample Size Considerations Using Dose Escalation with Overdose Control

Journal of Probability and Statistics ◽

10.1155/2012/692725 ◽

2012 ◽

Vol 2012 ◽

pp. 1-16 ◽

Cited By ~ 9

Author(s):

Mourad Tighiouart ◽

André Rogatko

Keyword(s):

Clinical Trial ◽

Clinical Trials ◽

Phase I ◽

Sample Size ◽

Dose Escalation ◽

Experimental Treatment ◽

Maximum Tolerated Dose ◽

Phase I Clinical Trials ◽

Number Of Patients ◽

Therapeutic Doses

The main objective of cancer phase I clinical trials is to determine a maximum tolerated dose (MTD) of a new experimental treatment. In practice, most of these trials are designed so that three patients per cohort are treated at the same dose level. In this paper, we compare the safety and efficiency of trials using the escalation with overdose control (EWOC) scheme designed with three or only one patient per cohort. We show through simulations that the number of patients per cohort does not impact the proportion of patients given therapeutic doses, safety of the trial, and efficiency of the estimate of the MTD. Additionally, we present guidelines and tabulated values on the number of patients needed to design a phase I cancer clinical trial using EWOC to achieve a given accuracy of the estimate of the MTD.

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