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Published on 9 June 2010

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Fixed dose versus patient specific dosing of anticancer agents

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Since the number of patients being treated for cancer increases as we live longer it is in all our interests to ensure that we explore any and all options which could keep us on the right side of the efficacy versus toxicity graph

Bruce Burnett
BSc (Hons) MMedSci RICR MRPharmS
Pharmacy Department
Glan Clwyd Hospital
Betsi Cadwaladr University Health Board
Rhyl, Wales

Individualised dosing of chemotherapy using body surface area is an entrenched methodology. Its limitations, whilst being acknowledged have never resulted in a generally accepted or widely used alternative.

The inaccuracies of the various equations are well documented and for any given individual the range of values produced is quite wide. Coupled with an inability to agree on whether ideal body weight, actual body weight or lean body weight should be utilised at extremes for cachectic or obese patients and the elderly it seems sensible to consider alternative descriptors of dose. The fact that current body surface area (BSA) based dosing gives such a range of responses within a population adds to cries for alternative dosing methodologies to be considered.[1] Indeed it has been the subject of a recent Drug and Therapeutics Bulletin.[2] The magnitude of dose differences associated with a range of BSA values is at most two fold with other factors, eg, excretion accounting for a four to 10 fold pharmacokinetic variation. This highlights the evidence from earlier studies relating BSA to pharmacokinetics of many cancer drugs and that for the majority there was little or no correlation.[3]

However the fact remains that BSA based dosing is not the only way to individualise dose. Pharmacokinetically guided dosing or therapeutic drug monitoring (TDM) is undertaken on a number of drugs and the paper by Yi Hon et al[4] summarises some of these drugs and the associated parameters measured to guide dosing. Carboplatin is probably the most widely used drug which we dose individually without BSA. The Calvert equation utilises accepted pharmacokinetic (PK) data, the area under the curve (AUC), toxicity for a given exposure and an individual’s renal function. An alternative to this is the Chatelut equation which utilises a population based pharmacokinetic equation to arrive at the dose. Population based pharmacokinetic dosing is being studied in a number of other drugs, eg, cladribine. Methotrexate serum levels are monitored during high dose therapy to minimise the toxicity by indicating the folinic acid doses required to ameliorate the toxic effects. More recently 5FU serum level monitoring has been shown to affect overall survival in the metastatic colorectal cancer setting.[5]

Phenotyping and genotyping are still in relative infancy but the potential benefit of isolating individuals to whom drug doses require adjustment or where alternative drugs should be used has obvious benefits. Similarly where clearance may be affected in groups of individuals, eg, docetaxel and cytochrome p450 CYP3A4, the use of probes (erythromycin breath test for docetaxel), or where response is likely (HER2 testing for trastuzumab and kras testing for cetuximab). This helps us move towards the holy grail of individualised dosing, maximising efficacy and minimising toxicity, especially if used alongside TDM for subsequent dose adjustment.

Individualised dosing has a number of benefits but is currently limited by:

  • Technology.
  • Agreement on the surrogate to measure (ie, is toxicity related to efficacy/response) having a test that is not affected by concomitant therapies – either for their cancer therapy or co-morbidities – variability of AUC for 5FU across a range of regimens has been shown.[6]
  • Availability of easy measurement, with an agreed frequency providing an accurate and timely result on which to act.

This is something which is currently either impossible or very difficult but as technology improves this will become more likely.

In the absence of accurate/available individualised dosing for chemotherapy drugs is it sensible to continue with BSA based dosing. This is particularly so since we arbitrarily adjust dose for elderly, less fit patients, cap doses etc with minimal evidence for such strategies.[7] The paediatric population provides an even greater test of alternatives to BSA based dosing due to a greater BSA range and other factors.

The limitations of BSA based dosing outlined previously led to the process of dose banding – essentially the same dose being given to a group of patients with similar BSAs. A ± 5% deviation from actual dose has been accepted by many, including a number of clinical trials in the UK. Within Wales a group is looking to extend the deviation range to ± 10% from actual dose. This is based on the range of errors associated with dose preparation – syringes, raw materials (BP limits and manufacture limits within batches) and considering whether any clinical limits to its adoption would remain, eg, adjuvant or curative therapies and clinical trials. When utilising a ± 10% values the majority of drugs can be limited to just 3 doses to cover a BSA range of 1.3–2.2m[2]. An example is shown in Table 1.

If we accept the ± 10% deviation argument then the flat fixed dosing option becomes more attractive. Comparison of flat fixed dosing versus BSA based dosing for a range of monoclonal antibodies has been explored by Wang et al.[8] They showed that the differences between the methods were minimal and Mathijssen[9] has shown very similar results when applied to a range of traditional cytotoxic agents. For some drugs/regimens flat dosing is already well accepted, eg, bleomycin within the BEP regimen. It is a methodology that we feel comfortable with for signal inhibitors, eg, erlotinib, sunitinib and imatinib and routinely outside cancer. The benefits of flat dosing are obvious – cheaper for manufacturers to produce fewer/one vial size, reduced risk of dose miscalculation, reduced in-process wastage during dose preparation and if extended stability of ready to use solutions can be determined, potentially there are reduced preparation risks and an easing of workload on our aseptic units. Flat fixed dosing studies in general have been small numbers or single institution data and still require wider validation.

However flat fixed dosing is not a panacea. We still need to resolve dose adjustment both for and in the absence of toxicity. An attractive option is that proposed by Gao et al.[10] This utilises dose clusters (comparable with dose bands), coupled with post therapy monitoring for toxicity and adapting the dose up or down within these clusters. They make use of genotyping/phenotyping when deciding a starting dose along with patient factors such as size, co-morbidities and previous drug exposure effects.

Most recently the concept of metronomic dosing brings alternative ideas.[12,13] The potential to give all patients the same dose, albeit generally a low dose, chronically and still maintain tumour minimisation (rather than clearance) should not be ignored. The mechanism(s) of action are not well defined but appear to be different from that seen for the same drug given at a standard dose and schedule, eg, an anti-angiogenesis effect, and indeed has been shown to induce an effect despite prior therapy and “resistance”. At such doses it may be that the effect/toxicity ranges for a population are much wider than that observed for traditional doses. This does not remove the need for TDM, in fact it may increase the need. Monitoring compliance may become paramount, especially if the compliance problems seen with drugs such as the aromatase inhibitors and imatinib were applied to metronomic dosing.

Metronomic dosing has been explored for a range of drugs and responses in heavily pretreated patients coupled with low toxicity profiles add credence to this method. Whether it is an example of flat fixed dosing or should be considered separately is open to debate.

Chronic treatment for chronic illness is the norm outside the cancer setting so why should chronic therapy for cancer be considered alien? There is an acceptance for maintenance monoclonal antibodies in some settings so are the goalposts simply moving. At least the concept warrants further investigation.

Ultimately any alternative dosing method proposed has to be generally accepted and not affect levels of toxicity or efficacy. For that to be proven it needs to be backed up with trial data from pre-registration studies and/or continued pharmacokinetic studies post-registration to ensure licensed dosing methods remain the best options. The more pharmacokinetic data collected post registration the more likely this will become. Already the FDA is requesting more PK data as part of the registration process and for at least one drug the manufacturer was requested to provide information on flat dosing. Similarly flat dosing had been considered for at least one large UK study – PICCOLO, but ultimately was not implemented. At least this shows a willingness to consider flat fixed dosing.

So what is happening in phase I studies? A summary of responses from a number of manufacturers is shown below in Table 2, and a summary from the major trial databases shows the wider post-registration studies. The methodology for determining dose calculation method has not changed. The general concept of increasing a flat dose until a maximum tolerated dose is reached and then examining PK variability in toxicity and/or response remains the norm for the majority of the industry.

The most interesting part of this is that flat dosing of monoclonal antibodies is becoming more common.

There are some trials using flat dosing but these tend to be for “standard regimens” which have had flat dosing for some time. Future fine-tuning of dosing beyond phase III studies is discussed in a paper by Rogatko et al[14] and suggest that it is possible to arrive at individualised dosing – but it needs key issues to be resolved:

  • Optimal theoretical methods for dose determination adjusted by covariates thoroughly understood, assessed and meticulously validated.
  • Efficient computer applications that can translate the results from the pages of biostatistical journals to patients being treated in standard and experimental therapies.
  • Well designed clinical studies that identify which patient characteristics are associated with a specific drug metabolism, and how these characteristics dynamically interact with it.

Given the leaps in scientific and computer technology over the last few years it may not be as distant as we may think.
Ultimately though there needs to be confidence in the method, so how would I want my chemotherapy doses calculated?

If available I would want my dose to be calculated individually based on phenotype/genotype and TDM, particularly for adjuvant/curative therapy where I want to maximise my chance of survival and minimise the toxicity associated with the treatment.

In the metastatic setting I am swayed by the metronomic dosing arguments, assuming that, like any conscientious pharmacist my compliance is excellent. Equally I would be happy to be flat dosed with those drugs where the benefit of one method over any other was questionable.

What this means is that there is not a single methodology which I would be happy with being applied to all drugs and all situations. For the moment the jury remains out but at least we are open to considering alternative strategies. Since the number of patients being treated for cancer increases as we live longer it is in all our interests to ensure that we explore any and all options which could keep us on the right side of the efficacy versus toxicity graph.

References
1. Egorin MJ. J Clin Oncol 2003;21:182-183.
2. Body surface area for adjustment of drug dose. Drug and Therapeutics Bulletin 2010;48(3):33-36.
3. Baker SD, et al. J Natl Cancer Inst 2002;25(30):4703-4704.
4. Yi Hon Y, et al. Clinical Chemistry 1998;44(2):388-340.
5. Gamelin E, et al. J Clin Oncol 2008;26(13):2099-2105.
6. Beumer JH, et al. HOPA Meeting 2010.
7. Field KM, et al. J Oncol Pharm Pract 2008;4(3):108-113.
8. Wang D, et al. J Clin Pharmacol 2009;49:1021-1024.
9. Mathijssen RHJ, et al. The Oncologist 2007;12:913-923.
10. Gao B, et al. Expert Opin Drug Metab Toxicol 2008;4(10):1307-1319.
11. Gurney H, et al. Clin Pharmacol Ther 2007;82(1):33-40.
12. Emmenegger U, et al. Onkologie 2007;30:606-608.
13. Scharovsky OG, et al. Curr Oncol 2009;16(2):7-15.
14. Rogatko A, Ghosh P, et al. Pharm Med 2008;22(6):345-350.



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