A New Method for Ethical and Efficient Evidence Generation for Off-Label Medication Use in Oncology (A Case Study in Glioblastoma)
- 1Blizard Institute, Queen Mary University of London, London, United Kingdom
- 2St Bartholomew’s Hospital, Bart’s Health NHS Trust, London, United Kingdom
- 3Care Oncology Clinic, London, United Kingdom
- 4Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
- 5Department of Science and Technology Studies, University College London, London, United Kingdom
In oncology, preclinical and early clinical data increasingly support the use of a number of candidate “non-cancer” drugs in an off-label setting against multiple tumor types. In particular, metabolically targeted drugs show promise as adjuvant chemo and radiosensitizers, improving or restoring sensitivity to standard therapies. The time has come for large scale clinical studies of off-label drugs in this context. However, it is well recognized that high-cost randomized controlled trials may not be an economically viable option for studying patent-expired off-label drugs. In some cases, randomized trials could also be considered as ethically controversial. This perspective article presents a novel approach to generating additional clinical data of sufficient quality to support changes in clinical practice and relabeling of such drugs for use in oncology. Here, we suggest that a pluralistic evidence base and triangulation of evidence can support clinical trial data for off-label drug use in oncology. An example of an off-label drug protocol brought to the clinic for glioblastoma patients is presented, along with preliminary retrospective data from the METRICS study (NCT02201381). METRICS is a novel participant-funded, open-label, non-randomized, single-arm real-world study designed to gather high-quality evidence on the safety, tolerability, and effectiveness of four off-label metabolically targeted medicines as an adjunctive cancer treatment for glioblastoma patients.
The economic and health burden of cancer are increasing, due in part to the rising incidence of many cancers, improving and earlier diagnosis, a growing and aging population, and the huge costs of new cancer treatments (Sullivan et al., 2011; Savage and Mahmoud, 2015; Fitzmaurice et al., 2018). The current system of drug research and development is increasingly considered economically unsustainable (Jackson and Sood, 2011), with an average delay from initial identification of a novel agent to regulatory approval of over 10 years (Paul et al., 2010), and costs from approximately $684 million to $2.7 billion per drug (DiMasi et al., 2016; Prasad and Mailankody, 2017). Furthermore, recent research has found that a significant number of novel cancer drugs enter the market without definitive evidence of benefit for overall survival or quality of life (Davis et al., 2017). Increasingly, healthcare systems are needing to deliver more with less, and mining the pharmacopeia of existing drugs for potential novel indications could offer an economically efficient path to new cancer treatments (Stoller et al., 2017; Prager et al., 2018).
Off-Label Drug Use in Oncology
Off-label use, and relabeling, of existing licensed medicines for treatment of cancer can be quicker and more cost-effective compared with untested drugs, as these licensed medicines already have well-established safety profiles, are often well tolerated, and usually have extensive preclinical and clinical evidence supporting their safe use in humans (Bertolini et al., 2015; Pantziarka et al., 2017; Sleire et al., 2017).
The anticancer properties of a variety of drugs, licensed for non-cancer–related conditions, are becoming widely recognized, and a number of initiatives exist that aim to generate evidence enabling the prescribing of off-label drugs in cancer (Pantziarka et al., 2014a; Hernandez et al., 2017). Even thalidomide has found new purpose in the treatment of myeloma (Palumbo et al., 2008). Promising evidence gathered from mechanistic, preclinical, and epidemiological studies has led to the extensive study of several off-label drugs for use in cancer. For example, the antihyperglycemic metformin and atorvastatin (a lipophilic statin) are two veteran licensed drugs with large epidemiological evidence bases built over decades supporting their use in cancer (Sahra et al., 2010; Altwairgi, 2015). Although some prospective randomized clinical trials are now underway investigating their use in cancer, progress in reaching this point has been slow. Prospective studies are generally small, focusing on mechanism of action or surrogate markers of efficacy (Chae et al., 2015; Chae et al., 2016). Significant barriers hamper efforts to provide sufficient quality evidence on the off-label use of these medicines in cancer, for gaining a marketing license for a new indication (Hernandez et al., 2017; Verbaanderd et al., 2017).
Despite the existence of promising evidence for statins and metformin in cancer, there is still a paucity of good quality prospective clinical data. One major reason for this is the lack of financial incentive to run large randomized controlled trials (RCTs) (Breckenridge and Jacob, 2018). As these drugs are off-patent, there is currently no financial incentive for companies to take on the research and development as well as clinical study expenditure (Breckenridge and Jacob, 2018). Thus, running large studies in off-label drugs for alternative indications, particularly those that are generically available, remain commercially nonviable and prevent the clinical development of promising anticancer treatments.
Across the breadth of the pharmacopeia, a prerequisite to the expansion of their off-label use and subsequent relabeling is the establishment of a high-quality evidence base for the new use. A major concern from purchasers and providers of treatments is the lack of good quality evidence base supporting off-label use (Dresser and Frader, 2009; Pfister, 2012). In the UK, clinicians are permitted to prescribe licensed medicines off-label, if they are satisfied that there is no other licensed alternative that would meet the patient’s needs and can be sure that there is sufficient evidence base and/or experience of using the medicine to show its safety and efficacy. However, the UK’s General Medical Council also makes it clear that when using medicines off-label, greater responsibility (and potential liability) falls on the prescribing clinician than when these medicines are used within the terms of their license. It is therefore understandable that clinicians may currently be reluctant to do this. In cancer, the rationale behind the Lord Saatchi’s failed medical innovation bill was to support such a practice by removing the perceived fear doctors have in assuming possible negligence action in common law by pursuing new off-label uses (Dyer, 2015). Although off-label prescribing is already commonplace in non-cancerous conditions, this is not the case for the use of non-chemotherapy drugs in oncology. Perhaps, an ethical off-label use for these safe and well-tolerated treatments might be their use as an adjunctive therapy on top of the current standard of care (SoC)? This would ensure that the patient receives off-label treatments only in addition to therapies currently labeled for cancer.
Creating an Evidence Base—Pluralistic Evidence and Triangulation
Regulatory authorities, healthcare purchasers, and clinicians require high-quality reliable evidence that can support the off-label use of drugs and relabeling for new indications. But how can this evidence be generated, in a situation where the funding of phase III trials is not commercially viable, and the ethics of placebo-controlled trials are increasingly under scrutiny, with their real-world relevance under question (Daugherty et al., 2008; Hamilton and Peppercorn, 2011; Corrigan-Curay et al., 2018)?
Pluralistic evidence as a methodological research approach can be used for clinical development programs where it is impossible, impractical, or unethical to follow a more traditional RCT-based approach (Tucker and Reed, 2008; Fives et al., 2017). In accordance with this, a pluralistic approach could therefore be applied when there is no commercial rationale for running a large randomized controlled clinical study to help establish causality for the efficacy of an off-label treatment in cancer.
Pluralistic evidence in support of a new product or new purpose is now in some cases accepted by regulatory authorities in place of an RCT. For example, during serious emergency epidemics, the World Health Organization now accepts the method called monitored emergency use of unregistered interventions, as seen in the use of Merck’s recombinant vesicular stomatitis virus vaccine in the recent Ebola outbreak in the Democratic Republic of Congo (Adebamowo et al., 2014; Calain, 2018). The European Medicines Agency approach to medicines adaptive pathways to patients also accepts the use of observational data to confirm early phase work on new products (Gannedahl et al., 2018). However, there remains the question of how to improve the quality of the evidence base, such that science can produce a more complete causal picture while minimizing the attendant biases, so that practitioners can learn what really works, and for whom.
To develop a pluralistic evidence base, all available sources and types of data should be considered, including in silico bioinformatics, laboratory studies on mechanisms of action, in vitro experiments on human biopsies, in vivo animal models, quality of life data, clinical trials of off-label drug use, etc. (Liu et al., 2013; Fives et al., 2017). As illustrated in the glioblastoma multiforme (GBM) case study, the pluralistic approach involves designing studies and generating new data from a range of sources to support causality. In this context, triangulation has an important role in using information from disparate sources in order to corroborate the findings of any single strategy or dataset (Munafò and Smith, 2018). Unlike traditional clinical study approaches, such as RCTs, triangulation does not aim to answer a predefined hypothesis or directly establish causality. Rather, triangulation can assess the external validity and strengthen interpretation of different datasets (Denzin, 1973; Rutherford et al., 2010).
In 2017, the European Union published a paper on the use of off-label drugs across Europe in all indications, including oncology, and set policy objectives to repurpose old drugs into new indications (Weda et al., 2017). This paper highlighted the importance of indication-driven trials with combinations of medicines and emphasized that the collection of real-world evidence is needed to give confidence in the efficacy of off-label treatments. In the light of this, we propose that a pluralistic, real-world approach with high external validity and auxiliary studies to improve internal validity offers a viable alternative to randomized controlled studies when these are not feasible. This approach can provide data of sufficient quality to “fill the evidence gap” for the efficacy and safety of off-label medicines in the treatment of cancer (Roche et al., 2014).
Off-Label Drug Use For Glioblastoma—The METRICS Study
We describe here our experience of developing a study for an off-label drug protocol for patients with cancer as an adjuvant therapy to SoC. The METRICS study (NCT02201381) is a novel open-label non-randomized, single-arm realworld study designed to gather high-quality evidence on the safety, tolerability, and effectiveness of four off-label metabolically targeted medicines (metformin, atorvastatin, mebendazole, and doxycycline) as an adjunctive cancer treatment.
There are two parts to METRICS: 1) a proof-of-concept retrospective analysis on the outcomes of GBM patients who have already received the off-label protocol and 2) a prospective analysis for newly enrolled patients with any cancer type, including GBM. Preliminary results from the retrospective analysis in patients with GBM are presented. These results provide the first of several planned datasets from different sources (collectively termed METRICS plus), which we believe in combination will provide good quality, robust evidence to help determine the causality of the adjuvant cancer protocol.
Along with the METRICS study, METRICS plus will comprise the following additional pillars of evidence required to enhance scientific value: 1) comparison of clinical endpoints with those of matched control patients (supplied by Public Health England) receiving SoC only; 2) two mechanism-of-action imaging studies pertaining to the new treatment; 3) animal data evaluating the protocol in a highly translatable setting (namely, a veterinary clinic treating canine cancer), using a randomized controlled design. These studies can be analyzed in their totality to determine the likely causality of the new treatment on the outcomes observed. The analysis of the summed datasets will be evaluated with Bayesian methodologies that can readily incorporate evidence from different sources.
Together, the studies of METRICS plus comprises the central pillars of what we term methodological pluralism. We suggest that such an approach may help to solve some of the difficulties with RCTs under these challenging circumstances and will also help provide a more complete picture of causality than a simple RCT would allow. It further allows greater scope for triangulation, which is becoming an increasingly important notion as trial data in many cases appear difficult to reproduce in the real-world setting (Munafò and Smith, 2018).
Rationale for the Adjuvant Drug Combination
The combination of off-label metabolic medicines used in METRICS (metformin, atorvastatin, mebendazole, and doxycycline) was chosen following an analysis of existing mechanistic and clinical data. These medications modulate interconnected intracellular pathways involved in cancer cell growth, proliferation, apoptosis, and angiogenesis, focusing on metabolic pathways (Sahra et al., 2010; Gazzerro et al., 2012; Pantziarka et al., 2014b; Ampuero and Romero-Gomez, 2015; Barbie and Kennedy, 2015). Additionally, metformin, doxycycline, and atorvastatin disrupt cancer cell membrane and signaling proteins and systems, including matrix metalloproteinases (Tang et al., 2013; Matusewicz et al., 2015; Peiris-Pagès et al., 2015; Babcook et al., 2016; Lei et al., 2017). Metformin also affects epigenetic upregulation of apoptotic factors and downregulation of oncogenic transcription factors such as STAT3 (overlapping with doxycycline for the latter mechanism) (Deng et al., 2012; Yu et al., 2017).
Importantly, preclinical studies show that adjuvant application of statins, metformin, or mebendazole can potentially enhance cytotoxic activity of standard anticancer treatments and reduce resistance (Cemeus et al., 2008; Jiang et al., 2014; Markowitz et al., 2017; Kipper et al., 2018). In aggressive non-small cell lung cancer, metformin has been shown to be radio and chemo-sensitizing in both preclinical and in early clinical studies (Troncone et al., 2017). Crucially, both metformin and doxycycline also disrupt the metabolic pathways of cancer stem cells, which are suspected to be the initiators of resistance and recurrence in tumors (Mohammed et al., 2013; Lamb et al., 2015; De Francesco et al., 2017). Mebendazole, metformin, and statins also indirectly modulate the immune system, and in addition, the latter two cause reduction in availability of glucose and low-density lipoproteins to cancer cells (Rosilio et al., 2014; Babcook et al., 2016; Blom et al., 2017). Supporting their use in brain cancers, metformin, atorvastatin, doxycycline and mebendazole have been shown to distribute through the blood brain barrier into the CNS (Nau et al., 2010; Wood et al., 2010; Bai et al., 2015; Koenig et al., 2017).
Numerous epidemiological and small prospective studies have concluded that further investigations are warranted for the use of metformin and statins in cancer (Chae et al., 2015; Chae et al., 2016), while mechanistic studies and case reports have advocated the use of doxycycline and mebendazole (Pantziarka et al., 2014b; Barbie and Kennedy, 2015). The combination of these four safe and well-tolerated medicines was selected to cover a diverse number of metabolic pathways, maximizing the potential efficacy across tumor types. In GBM, the need for new treatments is acute. GBM treatment is limited to surgery, radiotherapy, and oral temozolomide, resulting in a median overall survival (OS) of just 14.6 months for patients with GBM (Brodbelt et al., 2015).
Summary of Study Methodology and Retrospective Analysis of GBM Patients
Patients and Methods
Patients were recruited at a private clinic and gave written, informed consent in accordance with the Declaration of Helsinki. In addition to being made aware of the evidence base for the off-label drug use, its limitations, and the side-effect profile, patients were informed that by attending the clinic and paying for treatment, they were directly funding the research in which they were participating. Participant-funded research initiatives are a relatively new phenomenon, but their use is growing (Vayena et al., 2016). METRICS was approved by the East Midlands—South Leicester Research Ethics Committee in the UK. During the regulatory process, the UK Medicines and Healthcare Products Regulatory Agency and the national ethics committee defined a new study classification of “Interventional Service Evaluation” to describe the observational, pharmacodynamic data collection from patients.
The retrospective analysis consisted of 95 patients with advanced GBM stage IV (GBM) who attended the clinic between 2013 and 2016. Patients could be enrolled at any time from the presentation of their GBM onwards, i.e., newly diagnosed or at recurrence/progression. All patients continued to receive their SoC cancer treatments outside the clinic, mostly within the National Health Service. Every clinic visit generated correspondence to the patient’s primary care physician and the National Health Service oncology team (where consent to do so was obtained from the patient), from whom relevant patient data were obtained.
The primary endpoint was OS (defined as time from cancer diagnosis until death from any cause). Patients who were still alive at the time of analysis (clinical cutoff) and patients lost to follow-up were censored at their last clinical assessment date. All patient data were analyzed on an intention-to-treat basis and subjected to a time-to-event analysis, conducted by an independent third party (Cytel Inc, France). Full inclusion and exclusion criteria and further methodological details can be found at https://clinicaltrials.gov/ct2/show/NCT02201381.
Demographic and baseline characteristics were similar in this retrospective cohort to other published GBM cohorts (Stupp et al., 2005; Brodbelt et al., 2015). Mean (SD) age was 53.7 (13.52) years, and the majority of patients were male [65 patients (68.4%)]. The median time from diagnosis to starting the adjuvant protocol was 6.64 months. Although the study is ongoing, and this is not a planned interim analysis, the cohort as a whole (n = 95) had a median survival of 26.3 months and a two-year survival of 55.8%, with the censoring of patients shown in the Kaplan-Meier plot (Figure 1A). The SoC treatments received alongside the-off-label medications were: surgery, chemotherapy and radiotherapy (72.2%), biopsy, chemotherapy and radiotherapy (15.6%), radiotherapy alone (4.4%) and others (7.8%) (Figure 1A). The median OS for this unselected cohort of patients with GBM who received off-label medications alongside optimal SoC (i.e. surgery, chemotherapy and radiotherapy) was 27.1 months (95% CI 24.0; 37.6) from diagnosis, with a 2-year survival of 64.0%. These analyses compares favorably with other GBM cohorts receiving optimal SoC alone, with a median OS of 14.8 months (CI 14.2; 15.4) in the Public Health England dataset (Brodbelt et al., 2015) and 15.8 months (CI 13.2; 16.8) in a study by the European Organisation for Research and Treatment of Cancer (Stupp et al., 2005), with a 2-year survival of 28.7% and 26.5%, respectively (Figure 1B).
Figure 1 Overall survival of patients with glioblastoma multiforme (GBM) receiving the METRICS protocol in addition to optimal standard of care (SoC). (A) Kaplan–Meier plot of survival in patients with GBM receiving the METRICS protocol in addition to SoC, with uncensored patient numbers over time and the percentages of patients receiving the SoCs. The pie chart shows the distribution of SoC treatments. (B) Median overall survival and 2-year survival of patients receiving optimal SoC alongside the off-label medications in the METRICS study cohort compared with other GBM patient cohorts receiving optimal SoC. EORTC, European Organisation for Research and Treatment of Cancer (Stupp et al., 2005), Public Health England (Brodbelt et al., 2015).
This patient population is prone to experiencing symptoms related to both their disease as well as the SoC treatment received. Patients were closely monitored with respect to their symptomatology, serological and biochemical parameters, and performance status. The adjunctive combination of off-label medicines was generally well tolerated, with 85.3% (81/95) of this GBM cohort taking all four medications concurrently without issue.
The adverse event profile was as expected for these licensed medications. The most frequently reported conditions were gastrointestinal in nature (e.g., diarrhea, bloating, nausea), most commonly associated with metformin; and muscle and joint aches, linked with statin use. The overwhelming majority of adverse events reported were easily manageable and did not require interruption or cessation of the protocol. No drug-related SAEs were reported.
Of the 14.7% (14/95) of the patients who were not able to take all four protocol medications, 3.2% (3/95) comprised patients who were prevented from starting one or more of the medications due to clinical contraindications or drug interactions with existing medications they were taking. The other 11.5% (11/95) consisted of patients who either reported significant side effects attributable to one or more of the drugs or patients with potentially clinically relevant findings in one or more blood tests (i.e., full blood count, renal or liver function tests), which may or may not have been attributable to the protocol medications, but nevertheless required cessation of one or more of the protocol drugs for safety reasons.
The use of pluralistic evidence gathering in a real-world clinical setting offers a feasible alternative data collection model to break the financial deadlock preventing the study of off-label medications in oncology. The novel real-world study of off-label medicines presented here provides a proof of concept for generating clinical data when traditional RCTs are not an option. The initial findings with an adjuvant antimetabolic protocol in the setting of GBM SoC treatment are encouraging, with a median OS in the retrospective analysis of 27.1 months. A direct comparison with matched controls will be conducted in the future to establish whether this apparent effect on overall survival is different from standard of care. A pluralistic evidence base involves looking at disparate sources for generating data. In our example, a prospective phase of the METRICS study will form an additional part of the pluralistic evidence base, along with mechanistic clinical imaging studies in the case of GBM and an RCT of the adjuvant protocol for canine cancer. The external validity of these different studies can be assessed by triangulation, which also strengthens the interpretation of their findings.
East Midlands—Leicester South Research Ethics Committee (United Kingdom). Patients are asked to give informed consent prior to signing up to take the treatment protocol. Patients are allowed to withdrawal during the period between the initial consultation and the time they receive the treatment via recorded delivery, 2 to 3 days later. Some of the glioblastoma patients are frail, so they cannot attend face-to-face appointments. If this is the case, consultations are given via Skype (TeleHealth).
SE and SA conceived the Perspective together. SA did the database searches and identified the relevant articles around the off-label clinical use of non-cancer medicines in cancer. SE identified the relevant articles around the pluralism of evidence methodological approaches. SE and SA wrote the first draft and fully took part in redrafting and ensuring its scientific correctness. Both authors redrafted the report through several iterations and approved the final submitted version. PV and NM both contributed to data analysis. RS and SW both worked within the clinic managing the clinical service and enabling data collection. RB was the original inventor of the METRICS protocol.
SE was funded by NIHR UCL/UCLH Biomedical Research Centre and by EDCTP PANDORA-NET.
Conflict of Interest Statement
SA, PV, and NM work in the clinic running the METRICS study as private consultants paid directly by patients’ contributions.
None of them have shareholdings in the Care Oncology Clinic.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Dr. Alexandra Dedman provided professional medical writing assistance.
Adebamowo, C., Bah-Sow, O., Binka, F., Bruzzone, R., Caplan, A., Delfraissy, J.-F., et al. (2014). Randomised controlled trials for Ebola: practical and ethical issues. Lancet 384 (9952), 1423–1424. doi: 10.1016/S0140-6736(14)61734-7
Ampuero, J., Romero-Gomez, M. (2015). Prevention of hepatocellular carcinoma by correction of metabolic abnormalities: role of statins and metformin. World J. Hepatol. 7, 1105–1111. doi: 10.4254/wjh.v7.i8.1105
Bai, R. Y., Staedtke, V., Wanjiku, T., Rudek, M. A., Joshi, A., Gallia, G. L., et al. (2015). Brain penetration and efficacy of different mebendazole polymorphs in a mouse brain tumor model. Clin. Cancer Res. 21 (15), 3462–3470. doi: 10.1158/1078-0432.CCR-14-2681
Blom, K., Senkowski, W., Jarvius, M., Berglund, M., Rubin, J., Lenhammar, L., et al. (2017). The anticancer effect of mebendazole may be due to M1 monocyte/macrophage activation via ERK1/2 and TLR8-dependent inflammasome activation. Immunopharmacol. Immunotoxicol. 39, 199–210. doi: 10.1080/08923973.2017.1320671
Cemeus, C., Zhao, T. T., Barrett, G. M., Lorimer, I. A., Dimitroulakos, J. (2008). Lovastatin enhances gefitinib activity in glioblastoma cells irrespective of EGFRvIII and PTEN status. J. Neurooncol. 90, 9–17. doi: 10.1007/s11060-008-9627-0
Chae, Y. K., Yousaf, M., Malecek, M.-K., Carneiro, B., Chandra, S., Kaplan, J., et al. (2015). Statins as anti-cancer therapy; Can we translate preclinical and epidemiologic data into clinical benefit? Discov. Med. 20, 413–427.
Chae, Y. K., Arya, A., Malecek, M.-K., Shin, D. S., Carneiro, B., Chandra, S., et al. (2016). Repurposing metformin for cancer treatment: current clinical studies. Oncotarget 7, 40767–40780. doi: 10.18632/oncotarget.8194
Daugherty, C. K., Ratain, M. J., Emanuel, E. J., Farrell, A. T., Schilsky, R. L. (2008). Ethical, scientific, and regulatory perspectives regarding the use of placebos in cancer clinical trials. JCO 26, 1371–1378. doi: 10.1200/JCO.2007.13.5335
Davis, C., Naci, H., Gurpinar, E., Poplavska, E., Pinto, A., Aggarwal, A. (2017). Availability of evidence of benefits on overall survival and quality of life of cancer drugs approved by European Medicines Agency: retrospective cohort study of drug approvals 2009–13. BMJ 359, j4530. doi: 10.1136/bmj.j4530
De Francesco, E. M., Bonuccelli, G., Maggiolini, M., Sotgia, F., Lisanti, M. P. (2017). Vitamin C and doxycycline: a synthetic lethal combination therapy targeting metabolic flexibility in cancer stem cells (CSCs). Oncotarget 8, 67269–67286. doi: 10.18632/oncotarget.18428
Deng, X.-S., Wang, S., Deng, A., Liu, B., Edgerton, S. M., Lind, S. E., et al. (2012). Metformin targets Stat3 to inhibit cell growth and induce apoptosis in triple-negative breast cancers. Cell Cycle 11, 367–376. doi: 10.4161/cc.11.2.18813
Fitzmaurice, C., Akinyemiju, T. F., Lami, F. H. A., Alam, T., Alizadeh-Navaei, R., Allen, C., et al. (2018). Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 29 cancer groups, 1990 to 2016: a systematic analysis for the global burden of disease study. JAMA Oncol. 4 (11), 1553–1568. doi: 10.1001/jamaoncol.2018.2706
Gannedahl, M., Udechuku, A., Bending, M. W. (2018). Initiatives driving accelerated access to medicines in Europe: review of recent concepts and developments. Med. Access Point Care 2, 1–7. doi: 10.1177/2399202618761854
Gazzerro, P., Proto, M. C., Gangemi, G., Malfitano, A. M., Ciaglia, E., Pisanti, S., et al. (2012). Pharmacological actions of statins: a critical appraisal in the management of cancer. Pharmacol. Rev. 64, 102–146. doi: 10.1124/pr.111.004994
Hernandez, J. J., Pryszlak, M., Smith, L., Yanchus, C., Kurji, N., Shahani, V. M., et al. (2017). Giving drugs a second chance: overcoming regulatory and financial hurdles in repurposing approved drugs as cancer therapeutics. Front. Oncol. 7, 273. doi: 10.3389/fonc.2017.00273
Jiang, P., Mukthavaram, R., Mukthavavam, R., Chao, Y., Bharati, I. S., Fogal, V., et al. (2014). Novel anti-glioblastoma agents and therapeutic combinations identified from a collection of FDA approved drugs. J. Transl. Med. 12, 13. doi: 10.1186/1479-5876-12-13
Kipper, F. C., Silva, A. O., Marc, A. L., Confortin, G., Junqueira, A. V., Neto, E. P., et al. (2018). Vinblastine and antihelmintic mebendazole potentiate temozolomide in resistant gliomas. Invest. New Drugs 36, 323–331. doi: 10.1007/s10637-017-0503-7
Koenig, A. M., Mechanic-Hamilton, D., Xie, S. X., Combs, M. F., Cappola, A. R., Xie, L., et al. (2017). Effects of the insulin sensitizer metformin in Alzheimer’s disease: pilot data from a randomised placebo-controlled crossover study. Alzheimer Dis. Assoc. Disord. 31 (2), 107–113. doi: 10.1097/WAD.0000000000000202
Lamb, R., Ozsvari, B., Lisanti, C. L., Tanowitz, H. B., Howell, A., Martinez-Outschoorn, U. E., et al. (2015). Antibiotics that target mitochondria effectively eradicate cancer stem cells, across multiple tumor types: treating cancer like an infectious disease. Oncotarget 6, 4569–4584. doi: 10.18632/oncotarget.3174
Liu, Z., Fang, H., Reagan, K., Xu, X., Mendrick, D. L., Slikker, W., et al. (2013). In silico drug repositioning: what we need to know. Drug Discov. Today 18, 110–115. doi: 10.1016/j.drudis.2012.08.005
Markowitz, D., Ha, G., Ruggieri, R., Symons, M. (2017). Microtubule-targeting agents can sensitize cancer cells to ionizing radiation by an interphase-based mechanism. Onco. Targets Ther. 10, 5633–5642. doi: 10.2147/OTT.S143096
Mohammed, A., Janakiram, N. B., Brewer, M., Ritchie, R. L., Marya, A., Lightfoot, S., et al. (2013). Antidiabetic drug metformin prevents progression of pancreatic cancer by targeting in part cancer stem cells and mTOR signaling. Transl. Oncol. 6, 649–659. doi: 10.1593/tlo.13556
Nau, R., Sorgel, F., Eiffert, H. (2010). Penetration of drugs through the blood-cerebrospinal fluid/blood brain barrier for treatment of central nervous system infections. Clin. Microbiol. Rev. 23 (4), 858–883. doi: 10.1128/CMR.00007-10
Palumbo, A., Facon, T., Sonneveld, P., Blade, J., Offidani, M., Gay, F., et al. (2008). Thalidomide for treatment of multiple myeloma: 10 years later. Blood 111, 3968–3977. doi: 10.1182/blood-2007-10-117457
Pantziarka, P., Bouche, G., Meheus, L., Sukhatme, V., Sukhatme, V. P. (2014b). Repurposing drugs in oncology (ReDO)—mebendazole as an anti-cancer agent. Ecancermedicalscience 8, 443. doi: 10.3332/ecancer.2014.443
Paul, S. M., Mytelka, D. S., Dunwiddie, C. T., Persinger, C. C., Munos, B. H., Lindborg, S. R., et al. (2010). How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat. Rev. Drug Discov. 9, 203–214. doi: 10.1038/nrd3078
Peiris-Pagès, M., Sotgia, F., Lisanti, M. P. (2015). Doxycycline and therapeutic targeting of the DNA damage response in cancer cells: old drug, new purpose. Oncoscience 2, 696–699. doi: 10.18632/oncoscience.215
Prager, G. W., Braga, S., Bystricky, B., Qvortrup, C., Criscitiello, C., Esin, E., et al. (2018). Global cancer control: responding to the growing burden, rising costs and inequalities in access. ESMO Open 3, e000285. doi: 10.1136/esmoopen-2017-000285
Prasad, V., Mailankody, S. (2017). Research and development spending to bring a single cancer drug to market and revenues after approval. JAMA Intern. Med. 177, 1569–1575. doi: 10.1001/jamainternmed.2017.3601
Roche, N., Reddel, H., Martin, R., Brusselle, G., Papi, A., Thomas, M., et al. (2014). Quality standards for real-world research. Ann. Am. Thorac. Soc. 11 Suppl 2, S99–104. doi: 10.1513/AnnalsATS.201309-300RM
Rosilio, C., Ben-Sahra, I., Bost, F., Peyron, J.-F. (2014). Metformin: a metabolic disruptor and anti-diabetic drug to target human leukemia. Cancer Lett. 346, 188–196. doi: 10.1016/j.canlet.2014.01.006
Rutherford, G. W., McFarland, W., Spindler, H., White, K., Patel, S. V., Aberle-Grasse, J., et al. (2010). Public health triangulation: approach and application to synthesizing data to understand national and local HIV epidemics. BMC Public Health 10, 447. doi: 10.1186/1471-2458-10-447
Sahra, I. B., Marchand-Brustel, Y. L., Tanti, J.-F., Bost, F. (2010). Metformin in cancer therapy: a new perspective for an old antidiabetic drug? Mol. Cancer Ther. 9, 1092–1099. doi: 10.1158/1535-7163.MCT-09-1186
Stoller, C., Krähenbühl, S., Voiculescu, E. M. (2017). Value-added medicines: how repurposed medicines bring value to patients and pharmacists. GaBI Journal. 6 (3), 141–6. doi: 10.5639/gabij.2017.0603.027
Stupp, R., Mason, W. P., van den Bent, M. J., Weller, M., Fisher, B., Taphoorn, M. J. B., et al. (2005). Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. New Engl. J. Med. 352, 987–996. doi: 10.1056/NEJMoa043330
Sullivan, R., Peppercorn, J., Sikora, K., Zalcberg, J., Meropol, N. J., Amir, E., et al. (2011). Delivering affordable cancer care in high-income countries. Lancet Oncol. 12, 933–980. doi: 10.1016/S1470-2045(11)70141-3
Tang, H., Sampath, P., Yan, X., Thorne, S. H. (2013). Potential for enhanced therapeutic activity of biological cancer therapies with doxycycline combination. Gene Therapy 20, 770–778. doi: 10.1038/gt.2012.96
Troncone, M., Cargnelli, S. M., Villani, L. A., Isfahanian, N., Broadfield, L. A., Zychla, L., et al. (2017). Targeting metabolism and AMP-activated kinase with metformin to sensitize non-small cell lung cancer (NSCLC) to cytotoxic therapy: translational biology and rationale for current clinical trials. Oncotarget 8, 57733–57754. doi: 10.18632/oncotarget.17496
Vayena, E., Brownsword, R., Edwards, S. J., Greshake, B., Kahn, J. P., Ladher, N., et al. (2016). Research led by participants: a new social contract for a new kind of research. J. Med. Ethics 42 (4), 216–219. doi: 10.1136/medethics-2015-102663
Weda, M., Hoebert, J., Vervloet, M., Puigmarti, C., Damen, N., Marchange, S., et al., (2017). Study on off-label use of medicinal products in the European Union. European Union study report on off label use, 1–193.
Wood, W. G., Eckert, G. P., Igbavboa, U., Muller, W. E. (2010). Statins and neuroprotection: a prescription to move the field forward. Ann. N. Y. Acad. Sci. 1199, 69–76. doi: 10.1111/j.1749-6632.2009.05359.x
Keywords: off-label, glioblastoma, metformin, atorvastatin, mebendazole, doxycycline, metabolic targeting, pluralistic evidence
Citation: Agrawal S, Vamadevan P, Mazibuko N, Bannister R, Swery R, Wilson S and Edwards S (2019) A New Method for Ethical and Efficient Evidence Generation for Off-Label Medication Use in Oncology (A Case Study in Glioblastoma). Front. Pharmacol. 10:681. doi: 10.3389/fphar.2019.00681
Received: 11 February 2019; Accepted: 27 May 2019;
Published: 27 June 2019.
Edited by:Syed Nasir Abbas Bukhari, Al Jouf University, Saudi Arabia
Reviewed by:Tom Mikkelsen, Henry Ford Health System, United States
Thomas N. Seyfried, Boston College, United States
Sara A. Byron, Translational Genomics Research Institute, United States
Copyright © 2019 Agrawal, Vamadevan, Mazibuko, Bannister, Swery, Wilson and Edwards. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Samir Agrawal, firstname.lastname@example.org