Pallav Bhatnagar

Devesh Sinha

Richard A Parker

Dr Paul C Guyler
email: paul.guyler@southend.nhs.uk

Anthony O'brien

Intravenous thrombolysis in Acute Ischaemic Stroke: A

scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The safety and efficacy of intravenous (IV) thrombolysis with Alteplase, a reverse transcriptase Plasminogen Activator (tPA), licensed for use in a select population with acute ischemic stroke (AIS) is well established [START_REF] Wardlow | Thrombolysis for acute ischaemic stroke[END_REF]. In the United Kingdom about 30,000 people over 80 have AIS each year but they do not receive tPA because of their age and concern of causing symptomatic haemorrhage. The elderly (over 80 years) population have previously been subject to discrimination in trials of thrombolytic therapies. There have been attempts to justify their inclusion on the basis of data from non-randomised monitoring cohorts; Thrombolysis in the above 80 year old group is increasing and the SITS-MOST (Safe Implementation of Thrombolysis and Monitoring Study) database currently documents approximately 4000 such patients in 450 centres across 33 countries [2]. The question that clinicians are increasingly facing is: what is the safety and efficacy of IV thrombolytic therapy in the elderly population? The ongoing International Stroke Trial-3 (IST-3) [START_REF] Wardlow | Should more patients with acute ischaemic stroke receive Thrombolytic treatment?[END_REF] is likely to present its findings in April 2012 but until then stroke physicians need some information to help patients unable or unwilling to be part of thrombolysis trials to make an informed choice regarding off-licence use of alteplase. To try and help until such trials report we have performed a systematic review of current literature. The last systematic review published in 2006 by Engelter [START_REF] Engelter | Intravenous thrombolysis in stroke patients of ≥80 versus <80 years of age -a systematic review across cohort studies[END_REF] had a limited look at outcomes of IV thrombolysis in stroke patients of ≥ 80 versus < 80 years of age. Since then more evidence has become available that we have analysed in this paper to reach a clinical bottom line.

We believe this analysis is timely and justifies the continuation of trials that seek to obtain information on the balance of risk and benefit to populations presently excluded from treatment.

Methods

Methodology of systematic review

The existing literature was systematically searched by two independent authors (PB and DS) using National Information Resources facility for Medline (1950 onward), Embase (1980 onwards) and CINAHL (1981 onwards) databases until September 2010 for various terms related to "acute isch(a)emic stroke" and "thrombolysis" using thesaurus mapping and truncation as appropriate to maximise the scope of search. Following PICOS strategy was used to search the various databases.

Population: elderly patients ≥ 80 years old thrombolysed for AIS; Intervention: thrombolysis with tPA (Alteplase); Comparison: patients <80 years old thrombolysed for AIS; Outcomes: death, functional recovery by modified Rankin scale (mRS) and symptomatic intracranial haemorrhage (SICH) at three months; tudy design:comparative observational cohort studies; non-comparative case series, isolated case reports, reviews and comments were excluded. The leading journals and bibliographies of selected articles were hand searched. The studies satisfying inclusion and exclusion criteria were then individually appraised by PB and DS. Any disagreement was resolved by mutual discussion involving PG.

Statistical analysis

For each outcome of interest (SICH, mRS, and mortality at 3 months), a separate fixed-effects metaanalysis was performed utilizing the Mantel-Haenszel method in order to calculate an overall summary measure [START_REF] Sweeting | What to add to nothing? Use and avoidance of continuity corrections in meta-analysis of spare data[END_REF].

For all fixed-effects meta-analyses, it was assumed that the true effect sizes were the same for all studies, and any difference observed was simply due to sampling variation. A 2 χ test of heterogeneity (Q) was applied in each case to assess the fixed effects assumption. A 10% significance level was used for the test of heterogeneity.

For each of the outcomes of interest, forest plots were produced to show the effect sizes (odds ratios) for each of the relevant studies with an overall summary estimate (odds ratio) generated from the meta-analysis. Corresponding 95% confidence intervals (95% CI) were shown alongside the odds ratios. Publication bias was investigated for each meta-analysis using funnel plots with standard error on the y-axis and effect size on the x-axis.

In meta-analyses of observational studies, biased effect sizes for individual studies are an unfortunate possibility and therefore any biases in individual studies are likely to propagate into the overall summary measure. Therefore, Egger et al. suggest that exploring the possible sources of heterogeneity between observational study results should be an important feature of meta-analyses of observational studies [START_REF] Egger | Spurious precision? Meta-analysis of observational studies[END_REF]. We believe a non-significant test result of heterogeneity is not enough in order to have confidence in the overall summary measure since these tests often lack power so low levels of heterogeneity may still exist in practice. In addition, a hypothesis test provides no substitute for a thorough comparison of the studies. Therefore, with this in mind, in our systematic review we carefully assessed heterogeneity between studies and also tested the stability of the overall summary measure by performing a thorough sensitivity analysis for each meta-analysis.

The statistics software used was the ' Metafor' package [START_REF] Viechtbauer | Metafor: Meta-Analysis Package for R.R. package version 0[END_REF] in the R statistics software [START_REF] Development | R: A language and environment for statistical computing[END_REF].

Results

Results of systematic review:

The systematic search returned thirteen comparative cohort observational studies that appeared to answer the question, making it the most comprehensive review to date. Two papers [START_REF] Chen | Intravenous TPA for very old stroke patients[END_REF][START_REF] Tanne | Intravenous tissue plasminogen activator for acute ischaemic stroke in patients aged 80 years and older[END_REF] used similar methodology but calculated in-hospital death and/ or disability at discharge rather than at three months; hence only data on SICH was taken from them to maintain uniformity of analysis. One study [START_REF] Mouradian | Intravenous rt-PA for acute stroke: comparing its effectiveness in younger and older patients[END_REF] was alleged to have been included in a larger national multi-centre study [START_REF] Sylaja | Thrombolysis in patients older than 80 years with acute ischaemic stroke: Canadian Alteplase for Stroke Effectiveness Study[END_REF] in a previous review [START_REF] Engelter | Intravenous thrombolysis in stroke patients of ≥80 versus <80 years of age -a systematic review across cohort studies[END_REF]. We included this study and performed a sensitivity analysis to investigate the effect of this study on the overall summary estimates.

All studies had well defined methodology and collected data prospectively. Ringleb [START_REF] Ringleb | Thrombolytic therapy for acute ischaemic stroke in octogenarians: selection by magnetic resonance imaging improves safety but does not improve outcome[END_REF] and Mouradian [START_REF] Mouradian | Intravenous rt-PA for acute stroke: comparing its effectiveness in younger and older patients[END_REF] followed their local and national protocols. Subsequent sensitivity analysis excluding these studies revealed very little change in the overall summary odds ratios. The majority of authors attempted to compare most of the known, potentially confounding, baseline characteristics of the two populations except Ringleb [START_REF] Ringleb | Thrombolytic therapy for acute ischaemic stroke in octogenarians: selection by magnetic resonance imaging improves safety but does not improve outcome[END_REF]and Berrouschot [START_REF] Berrouschot | Outcome and severe hemorrhagic complications of intravenous thrombolysis with tissue plasminogen activator in very old (≥80 years) stroke patients[END_REF] who compared only few characteristics such as baseline NIHSS, sex and time to treatment. Four authors [START_REF] Sylaja | Thrombolysis in patients older than 80 years with acute ischaemic stroke: Canadian Alteplase for Stroke Effectiveness Study[END_REF][START_REF] Ringleb | Thrombolytic therapy for acute ischaemic stroke in octogenarians: selection by magnetic resonance imaging improves safety but does not improve outcome[END_REF][START_REF] Toni | Intravenous thrombolysis with rt-PA in acute ischaemic stroke patients aged older than 80 years in Italy[END_REF][START_REF] Engelter | Thrombolysis in stroke patients aged 80 years and older: Swiss survey of IV thrombolysis[END_REF] did not mention if they included consecutive thrombolysed patients but it appears from their results that they did.

Seven authors (table 1) mentioned protocol violations and amongst them Tanne [START_REF] Tanne | Intravenous tissue plasminogen activator for acute ischaemic stroke in patients aged 80 years and older[END_REF] had protocol violations significantly different between the two groups. Losses to follow up were accounted for in two studies at three months. It was not considered relevant for Tanne [START_REF] Tanne | Intravenous tissue plasminogen activator for acute ischaemic stroke in patients aged 80 years and older[END_REF] and Chen [START_REF] Chen | Intravenous TPA for very old stroke patients[END_REF] as the outcome measures of death and disability were calculated at discharge. The individual characteristics of the studies are tabulated in table 1 and details of each study including results are mentioned in table 2. Uyttenboogaart [START_REF] Uyttenboogaart | Routine thrombolysis with intravenous tissue plasminogen activator in acute ischaemic stroke patients aged 80 years or older: a single centre experience[END_REF] performed multivariate analysis to adjust for possible confounders and eight other studies [10-12, 14-16, 18, 19, 21] performed logistic regression analysis to adjust for the differences in important baseline characteristics and to identify predictors of favourable and/or poor outcomes. Our analysis showed that there was little evidence to suggest publication bias in funnel plots for the three outcomes (figure 1). a) Death at 3 months Mortality at three months was reported by eleven studies. Results by Mouradian [START_REF] Mouradian | Intravenous rt-PA for acute stroke: comparing its effectiveness in younger and older patients[END_REF] were excluded from the meta-analysis as they reported only stroke related deaths at three months. The combined (fixed) OR as represented by a diamond at the bottom of the plot (figure 2), was calculated to be 2.77, 95% CI 2.25 -3.40 from ten studies suggesting an increased likelihood of death at three months in the thrombolysed over 80 group. Including Mouradian [START_REF] Mouradian | Intravenous rt-PA for acute stroke: comparing its effectiveness in younger and older patients[END_REF] in the analysis did not change the results significantly with the odds ratio being 2.80 (95% CI 2.29 -3.43).

The test for heterogeneity gave a Q score of 11.98 on 9 degrees of freedom. This corresponds to a pvalue of 0.21, which is non-significant at the 10% level. Therefore, there is insufficient evidence of any heterogeneity between studies.

Sensitivity analysis performed to adjust for minor variations amongst the studies showed little change in the overall summary odds ratio and our conclusions remained the same in each case.

When the Gomez-Choco [START_REF] Gomez-Choco M | The response to IV rt-PA in very old stroke patients[END_REF] paper was removed however, the overall summary estimate of the odds ratio increased by 0.15, but our overall conclusions did not change. b) Functional outcome at 3 months Analysis was performed on eight studies that reported favourable outcome as mRS 0 -1. Three studies were not included in the statistical analysis [START_REF] Mouradian | Intravenous rt-PA for acute stroke: comparing its effectiveness in younger and older patients[END_REF][START_REF] Toni | Intravenous thrombolysis with rt-PA in acute ischaemic stroke patients aged older than 80 years in Italy[END_REF][START_REF] Gomez-Choco M | The response to IV rt-PA in very old stroke patients[END_REF] as they reported favourable outcome as 0 -2. The combined (fixed) odds ratio was calculated to be 0.49 (95% CI 0.40 -0.61) suggesting that patients over 80 are not as likely to achieve favourable outcome as patients less than 80 years old thrombolysed for AIS.

The test for heterogeneity gave a Q score of 6.54 on 7 degrees of freedom. This corresponds to a pvalue of 0.48, which is non-significant at the 10% level. Therefore, there is insufficient evidence of any heterogeneity between studies.

A sensitivity analysis was performed which showed very little change in the overall summary odds ratio and our conclusions remained the same.

We considered performing a meta-analysis of favourable outcome as mRS 0-2. That is, we only included the Toni [START_REF] Toni | Intravenous thrombolysis with rt-PA in acute ischaemic stroke patients aged older than 80 years in Italy[END_REF], Gomez-Choco [START_REF] Gomez-Choco M | The response to IV rt-PA in very old stroke patients[END_REF] and Mouradian [START_REF] Mouradian | Intravenous rt-PA for acute stroke: comparing its effectiveness in younger and older patients[END_REF] papers. However, the test for heterogeneity resulted in a Q-statistic of 5.49 on 2 degrees of freedom with a corresponding p-value of 0.06. Therefore, the test was significant at the 10% level, and it was considered invalid to combine the results.

c) Incidence of symptomatic intracranial haemorrhage (SICH)

Studies were noted to have employed two different definitions of SICH. Five studies [START_REF] Tanne | Intravenous tissue plasminogen activator for acute ischaemic stroke in patients aged 80 years and older[END_REF][START_REF] Ringleb | Thrombolytic therapy for acute ischaemic stroke in octogenarians: selection by magnetic resonance imaging improves safety but does not improve outcome[END_REF][START_REF] Engelter | Thrombolysis in stroke patients aged 80 years and older: Swiss survey of IV thrombolysis[END_REF][START_REF] Uyttenboogaart | Routine thrombolysis with intravenous tissue plasminogen activator in acute ischaemic stroke patients aged 80 years or older: a single centre experience[END_REF] and 20] used definition as utilised by NINDS stroke study group [START_REF] The | Tissue plasminogen activator for acute ischemic stroke[END_REF] where a haemorrhage was considered symptomatic if it was not seen on an previous CT scan and there had subsequently been a decline in neurological status. Eight [9,11,12,14, 15,18, 19 and 21] used definition as utilised by ECASS III trial [START_REF] Hacke | Thrombolysis with alteplase 3 to 4.5 hours after acute ischaemic stroke[END_REF] where SICH was defined as an intracranial bleed on CT scan with a decrease in the NIHSS score of 4 or more points.

The combined (fixed) OR, of all studies, was calculated to be 1.31, 95% CI 0.93 -1.84 suggesting no significant difference in the risk of SICH between the two age groups.

The test for heterogeneity gave a Q score of 6.41 on 12 degrees of freedom. This corresponds to a pvalue of 0.89, which is non-significant at the 10% level. Therefore, there is insufficient evidence of any heterogeneity between studies.

A thorough sensitivity analysis was performed by sequentially removing individual studies that had minor variations and it showed very little change in the overall summary odds ratio and our overall conclusions remained the same.

Discussion

The question whether thrombolysis is safe and effective in the elderly population aged over 80 years requires an adequately powered randomised controlled trial (RCT) such as the ongoing IST-3 [24].

Various studies, appraised in this review, attempted to obtain indirect evidence to answer the question by comparing their experience of off-licence use of IV thrombolysis in patients over 80 years of age with those in younger patients.

In the absence of an RCT such comparative cohort observational studies are the next most appropriate research design to be considered as patients over 80 years old are increasingly being provided thrombolysis as treatment for AIS.

The mean proportion of over 80 patients achieving favourable outcome of 0-1 was found to be 26.7% (range 16.1 -43.5%; standard deviation of 8.1) on pooled analysis of the data from the eleven studies included in this review.. The estimate of effect on SICH is more problematic to assess and has lowest power because of two separate definitions across studies and small number of events. The confidence intervals are also wide and can not exclude either a small reduction or an increase in incidence of SICH. However regardless of these two different definitions there was no significant difference noted amongst the two groups. Systematic reviews of observational studies have the inherent weakness that in the absence of randomisation they are particularly prone to biases; for example, selection bias. It is not unreasonable to suppose that many (if not all) of the studies in the meta-analysis suffer from this kind of bias whereby only those elderly patients who seem particularly healthy or suitable are entered in the study cohort, while those in the younger age group are selected less carefully. If this is true, then this will help to mask an increased risk of SICH for the older age group if it exists. The pooled analysis of appraised studies in this review shows case fatality ranging from 6.1% to 45.2% (mean 28.3%; standard deviation of 12.0) in the thrombolysed ≥ 80 group. Given these data are from reasonably representative samples of hospital patients, there must be a considerable, but variable, amount of case selection (selection bias) in the included cohorts. For these reasons, the results provide no substitute for a randomised control trial. Moreover, if confounding bias is present such that there are one or more variables related to both age group and SICH which are not consequences of either, they can serve to distort the relationship between age group and SICH. The confounding variables which induce a negative association between age group and SICH are of particular interest since these will minimise any existing relationship between the variables. Some authors used logistic regression analysis to adjust for confounders, and this method is to be recommended. However, unadjusted-for confounders may still remain.

Publication bias may be an additional cause of bias whereby authors may be less willing to publish results which show a significant difference in SICH if it is against their a priori beliefs. However, the funnel plots did not suggest any evidence of publication bias for each of the outcomes in this metaanalysis.

Another potential bias is caused by loss to follow-up and/or protocol violations; especially differential loss to follow-up between the age groups. If those in the <80 age group are followed-up less rigorously for example, and if loss to follow-up is mainly caused by greater health or mobility, then the proportion of patients in the <80 age group associated with SICH or mortality will be biased upwards and this could contribute to a non-significant result. Unfortunately, only Parnetti [START_REF] Parnetti | Efficacy of thrombolytic (rt-PA) therapy in old stroke patients: The Perugia stroke unit experience[END_REF] and Meseguer [START_REF] Meseguer | Determinants of outcome and safety of intravenous rt-PA therapy in the very old: a clinical registry study and systematic review[END_REF] described and compared losses to follow up.

In some studies, small numbers of elderly patients over 80 may cause problems, not only because it suggests selection bias, but also because the sample size itself means that there may be low power to detect differences which are clinically significant. This means that the Type II error rate would be high. This is also likely to be a feature of poorly designed and conducted studies. Therefore, in order to have any confidence in non-significant results it is especially important that studies are wellconducted.

One important weakness which was not mentioned in any of the studies except Engelter [START_REF] Engelter | Thrombolysis in stroke patients aged 80 years and older: Swiss survey of IV thrombolysis[END_REF] is that of the disadvantage associated with using the dichotomization of <80 years and ≥ 80 years. The motivation for this is clear, since the NINDS study did not include many patients over 80, but it still seems unsatisfactory that an 80 year old patient should be treated any differently to a 79 year old. In reality, the relationship between age and outcome is likely to be gradual as calculated by Berrouschot [START_REF] Berrouschot | Outcome and severe hemorrhagic complications of intravenous thrombolysis with tissue plasminogen activator in very old (≥80 years) stroke patients[END_REF] and any dichotomization of a continuous variable such as age is likely to result in a loss of power to detect a significant association. This is especially the case for example, if probability of SICH increases only very slightly for patients 80-85 years and then increases more substantially after this. The problems associated with the dichotomization provides another reason why an RCT in the elderly population is better than any comparison between two age groups <80 and ≥ 80 for tPA treated patients.

Conclusion

Elderly patients, thrombolysed for AIS, appear to have a lower probability of gaining favourable outcome and a higher mortality rate compared to younger patients less than 80 years of age.

However, the rate of SICH is not significantly worse in the ≥80 year olds when compared to younger patients.

These data support the rationale for recruiting patients aged over 80 into ongoing trials comparing thrombolysis with control. For patients who refuse or cannot be randomised, this meta-analysis provides useful information on the potential risks and benefits of using Alteplase off-licence. • 20(5.3%)

• 6(6.7%) P>0.05

• 158(41.8%)

• 17(18.9%) P value n.a.

• 48(12.7%)

• 26(28.9%)

Figure 2 :

 2 Figure 2: Forest plots showing effect of age group on SICH, risk of death at three months/ 90 days and probability of favourable outcome -odds ratio (fixed effects) meta-analysis plots. Right hand side of plot corresponds to an increased risk for patients ≥ 80 years old relative to those <80 years.

Table 1 :

 1 Characteristics of appraised studies NINDS: National Institute of Neurological Diseases and Stroke EUSI: European Stroke Initiative recommendations for stroke management -update 2003

Table 2 :

 2 Results of individual studies

	Studies	Centre	Prospective data collection	Protocol Protocol violations	Losses to follow up
	Toni (15)	Multi, Italy	√	NINDS	Described (p=0.59)	Not
						described
	Uyttenboogaa	Single,	√	NINDS	Not described	Not
	rt (17)	Netherlands				described
	Gómez-Choco	Single, Spain	√	NINDS	Not described	Not
	(19)					described
	Meseguer (18)	Single, France	√	NINDS	Described (p=1.0)	Described
	Ringleb (13)	Single,	√	Local	Not described	Not
		Germany				described
	Mouradian	Single, Canada	√	National	Not described	Not
	(11)					described
	Berrouschot	Multi,	√	NINDS	Described (p=0.67)	Not
	(14)	Germany				described
	Engelter (16)	Multi,	√	NINDS	Described (p=0.43)	Not
		Switzerland				described
	Sylaja

  Figure 1: Funnel plots to assess possible publication bias corresponding to meta-analyses performed for SICH, mortality at three months and functional outcome on mRS respectively.

	• <80	• 49	patients had incidence of			P=0.69 • 1 (2%)			P<0.001 • 20 (40.8%)	P=0.01 • 4 (8.2%)
	Berrouschot • <80 • ≥80 • ≥80 Tanne • <80 • ≥80	• 190 • 38 (16%) • 23 (30.5%) • 159 • 30 (15.8%) Standard Error 1.437 1.078 0.719 0.359 0.000	higher baseline NIHSS Only baseline NIHSS compared p=0.115 previous stroke and anti-platelet therapy in ≥80 group Higher incidence of diabetes and current smokers in SICH			• 5(2.6%) • 1(2.6%) P=1.0 • 1 (4.3%) p>0.05 • 10 (6%) • 1 (3%) P=0.99		• 89(46.8%) • 10(26.3%) P=0.021 • 10 (43.4%) p>0.05 Not calculated at three months Standard Error van Oostenbrugge RJ et al. , 2006 Not calculated at • 10(5.3%) • 8(21.1%) P=0.004 • 3 (13.0%) p>0.05 three months Sylaja PN et al. , 2006 Engelter ST et al. ,2005 Berrouschot J et al. , 2005 Ringleb PA et al. , 2007 Meseguer E et al. , 2008 Gomez-Choco M et al. , 2007 Uyttenboogaart M et al. , 2007 Toni D et al. , 2007 0.810 0.607 0.405 0.202 0.000 Death at 90 days	3.55 [ 1.67 , 7.51 ] 2.45 [ 1.81 , 3.32 ] 3.32 [ 1.54 , 7.18 ] 4.80 [ 1.75 , 13.14 ] 2.79 [ 1.62 , 4.83 ] 2.97 [ 0.97 , 9.04 ] 0.58 [ 0.15 , 2.16 ] 5.71 [ 2.31 , 14.07 ] 4.36 [ 1.99 , 9.53 ]
	Engelter • <80 Uyttenboogaart M et al., 2007 • 287 Toni D et al., 2007	More females, <80 and higher pre--3 -2 -1	0	• 24 (8%) 1 2 2.87 [ 0.61 , 13.55 ] 3 1.01 [ 0.21 , 4.79 ]	• 107 (37%) -1 Parnetti L et al. , 2006	0	• 35 (12%) 1	2	3	1.69 [ 0.35 , 8.25 ]
	• ≥80 Meseguer E et al., 2008 • 38 Gomez-Choco M et al., 2007 Ringleb PA et al., 2007 Mouradian MS et al., 2005 (12%) Sylaja • <80 • ≥80 • 865 • 270 (23.8%) Berrouschot J et al., 2005 Chen • <80 • ≥80 • 127 • 56 (44.1%) Engelter ST et al.,2005 Standard Error Combined (fixed) Tanne D et al., 2000 Chen C-I et al., 2005 Sylaja PN et al., 2006 Odds Ratio [ 95% Confidence Interval ] higher SBP, cardio-embolic • 5 (13%) treatment Observed outcome for SICH 1.11 [ 0.27 , 4.63 ] 1.95 [ 0.47 , 8.04 ] systolic BP in elderly 1.28 [ 0.50 , 3.28 ] 1.63 [ 0.34 , 7.79 ] source and AF in elderly group P=0.36 More females, higher incidence of HTN, AF, IHD, CHF, higher • 40 (4.6%) • 12 (4.4%) P=1.0 1.00 [ 0.11 , 8.81 ] More females and cardio-embolic strokes in elderly group 1.66 [ 0.59 , 4.65 ] • 8 (6.3%) • 4 (7.1%) P=0.90 Not calculated at three • 11 (29%) Observed outcome for mortality Combined (fixed) • 12 (32%) 0.10 1.00 4.00 P=0.37 P=0.005 • 40.2% • 25.9% P=0.001 • 18.2% • 35.3% Combined (fixed) P=0.001 Odds Ratio [ 95% Confidence Interval ] 16.00 Higher mortality if aged 80+ Higher mortality if aged <80 months Not calculated at three months 1.31 [ 0.93 , 1.84 ] 0.10 1.00 4.00 16.00 0.51 [ 0.06 , 4.17 ] 1.14 [ 0.33 , 3.97 ] 0.96 [ 0.50 , 1.86 ] SICH more likely if aged 80+ SICH more likely if aged <80 Parnetti L et al. , 2006 van Oostenbrugge RJ et al. , 2006 Sylaja PN et al. , 2006 Engelter ST et al. ,2005 Berrouschot J et al. , 2005 Ringleb PA et al. , 2007 Meseguer E et al. , 2008 Uyttenboogaart M et al. , 2007 0.527 0.395 0.263 0.132 0.000 Parnetti L et al., 2006 van Oostenbrugge RJ et al., 2006 2.18 [ 0.13 , 36.51 ] 4.22 [ 1.08 , 16.46 ] Favourable outcome (MRS 0-1)	2.77 [ 2.25 , 3.40 ] 0.49 [ 0.40 , 0.61 ] 1.12 [ 0.41 , 3.04 ] 0.45 [ 0.22 , 0.95 ] 0.52 [ 0.38 , 0.70 ] 0.69 [ 0.33 , 1.44 ] 0.41 [ 0.19 , 0.88 ] 0.32 [ 0.18 , 0.57 ] 0.63 [ 0.23 , 1.74 ] 0.34 [ 0.12 , 0.96 ]
			pre-treatment -2.0 -1.5	-1.0		-0.5	0.0	0.5
			SBP and baseline Observed functional outcome on mRS			0.10 Odds Ratio [ 95% Confidence Interval ] 0.50 2.00 8.00
										Favourable outcome more likely if aged <80	Favourable outcome more likely if aged 80+
			NIHSS in ≥80
			group and less
			smoker and
			cholesterol
	Oostenbrugge
	• <80	• 139	More females,			• 4 (2.9%)			• 62 (45%)	Not available
	• ≥80	• 45	higher incidence of			• 5 (11.1%)			• 12 (27%)	• P<0.001
		(24%)	IHD and CHF
	Mouradian		in ≥80 group	OR 4.2 (95%CI:1.08-	mRS 0-2 OR 2.2 (95%CI:1.06-	CVA related deaths
	• <80	• 65	Higher and were less			• 4(6.2%) 16.46)			• 38(58.5%) 4.46)	• 7(10.8%)
			incidence of often smokers
	• ≥80	• 31	CHF and HTN			• 3(9.7%)			• 5(16.1%)	• 10(32.3%)
			in elderly.
	Parnetti		More elderly
			Higher


Stephanie Katzenmaier

Christoph Markert

MD Gerd Mikus
email: gerd.mikus@med.uni-heidelberg.de

Proposal of a new limited sampling strategy to predict CYP3A activity using a partial AUC of midazolam

Midazolam metabolic clearance to 1'-hydroxymidazolam is an accurate measure for CYP3A activity which requires extensive plasma and urine sampling. The objective of this study was to find a new limited sampling strategy (LSS) to predict midazolam metabolic clearance to 1'hydroxymidazolam and subsequently CYP3A activity in an easy and reliable way, reducing costs and labour.

METHODS

The development of this LSS is based on data of an in vivo drug-drug interaction study carried out in our clinical research unit. Various partial AUCs of midazolam were calculated and correlated with metabolic clearance of midazolam to 1'-hydroxymidazolam by non-linear regression. The correlation with highest r 2 -values was chosen in order to predict the midazolam metabolic clearance. Statistical significance of this method was verified by calculating the 95% confidence interval of the differences (%) between predicted and measured metabolic clearance of midazolam to 1'-hydroxymidazolam.

RESULTS

The midazolam partial AUC from 2-4 hours after oral administration correlated best with metabolic clearance of midazolam to 1'-hydroxymidazolam with r 2 = 0.9816. This partial AUC comprised of four midazolam concentrations at 2, 2.5, 3 and 4 hours after oral administration of a midazolam solution. The 95% confidence interval of the differences between predicted metabolic clearance and measured metabolic clearance of midazolam to 1'hydroxymidazolam was [-0.97 to +13.2].

CONCLUSION

Introduction

Cytochromes P (CYPs) 450 represent a multifaceted family of heme-containing enzymes belonging to the group of monooxygenases accountable for phase-1-metabolism. Mammalian cytochromes are membrane proteins taking an active part in biosynthesis and metabolism of many physiological active substances. Moreover these enzymes are capable of catalysing biotransformation and metabolism of substances of foreign origin like drugs and toxins [1].

Cytochrome P 450 3A (CYP3A) is the most abundant enzyme of CYPs in the liver and the gut, it metabolises approximately 50% of currently available drugs and accounts for 30% of the total CYP enzymes in hepatic microsomes. A large number of drugs interfere with CYP3A as they may act as inducers, substrates or inhibitors of this enzyme.

To determine the activity of CYP3A midazolam is often used as a probe drug for this enzyme [2]. Midazolam belongs to the benzodiazepines family and is frequently used for anaesthetic induction, narcotic premedication and sedation of patients prior to surgery or other invasive medical procedures. It is extensively metabolised by CYP3A to its primary metabolite, 1'hydroxymidazolam and to minor metabolites, 4'-hydroxymidazolam and 1, 4'dihydroxymidazolam. Only 1 % of midazolam is eliminated unchanged in urine. Main urinary metabolite is 1'-hydroxymidazolamglucuronide, which amounts to 63-80 % of the administered dose within 24 hours. Conjugates of 4'-hydroxymidazolam and 1',4'dihydroxymidazolam amount to 3 % and 1 % in urine [3]. Orally administered midazolam is used to assess the total -intestinal and hepatic -CYP3A activity.

Usually, CYP3A activity is tested before and during treatment with a potential inducer or inhibitor. To assess the time course of inhibition or induction frequent midazolam testing would be required. The most predictive and reliable parameter to assess CYP3A activity is the metabolic clearance to 1'-hydroxymidazolam [4]. Therefore, a full pharmacokinetic profile (AUC 0-∞ ) and the urinary excretion of 1'-hydroxymidazolam and its glucuronide over at least 24 hours after single dose administration are required. Hence the use of midazolam as a probe We here propose a new limited sampling strategy to predict the midazolam metabolic clearance to 1'-hydroxymidazolam and subsequently CYP3A-activity in an easy and reliable way using much less plasma samples and no urine sampling. Limited sampling is a useful strategy to simplify proceedings and reduce costs and labour. This is relevant for the clinical application of CYP3A phenotyping.

Methods

The development of this LSS is based on data of an in vivo drug-drug interaction study carried out at the clinical research unit of the Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg. In this study twelve participants were included. The protocol was approved by the competent authority (BfArM, Germany) and the Ethics Committee of the University of Heidelberg, written informed consent was obtained from each subject prior to study-related procedures. On the first occasion they received midazolam in order to determine the baseline metabolic clearance of midazolam to 1'hydroxymidazolam. Thereafter efavirenz (400 mg p.o. once daily) was administered over 14 days to study the inducing effect of efavirenz on CYP3A. Midazolam was given orally on day 1 and 14 of this period. After an overnight fast 3 mg midazolam (Dormicum ® Roche 5mg/5ml, solution for injection or infusion) were administered in 100 ml tap water. 4 hours thereafter a standard meal was provided for each subject. Blood samples were taken after midazolam at 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10 hours and urine was collected completely over 24 hours. Concentrations of midazolam and 1'-hydroxymidazolam in plasma and urine were quantified by high performance liquid chromatography coupled to tandem mass spectrometry after modification of a published method [5] as described elsewhere [6].

The LC/MS/MS system (Thermo Fisher, Dreieich, Germany) consisted of a Surveyor Plus HPLC System and a triple stage quadrupole mass spectrometer (TSQ 7000 with API-2 ion source and performance kit). For quantification of the drugs a Phenomenex Max RP column (150 x 2 mm, 4 µm, Phenomenex, Aschaffenburg, Germany) with integrated guard column was used at 40°C for chromatographic separation. The bioanalytical method was validated according to the recommendations of the FDA in the range of 0.61-460 nmol/L for midazolam and 0.59-439 nmol/L for 1'-hydroxymidazolam in plasma and 1.53-153 nmol/L for midazolam and 73.2-7315 nmol/L for 1'-hydroxymidazolam in urine. Lower limits of quantification for midazolam and 1'-hydroxymidazolam in plasma were 0.61 and 0.59 nmol/l, respectively, (with accuracy/precision of -4.2%/5.0% and +11.6%/4.6%, respectively); lower limit of quantification in urine was 1.53 nmol/l for midazolam and 72.2 nmol/l for 1'dydroxymidazolam (accuracy/precision of +8.0%/7.9% and +3.8%/7.1%, respectively).

Metabolic clearance of midazolam to 1'-hydroxymidazolam was calculated as the amount of total (conjugated + unconjugated) 1'-hydroxymidazolam excreted in urine (0-24 h) divided by midazolam AUC 0-∞ .

In order to predict metabolic clearance of midazolam to 1'-hydroxymidazolam various partial midazolam AUCs were calculated and correlated with the metabolic clearance of midazolam to 1'-hydroxymidazolam. Stepwise multiple regressions were used to identify optimal time points and intervals of midazolam plasma concentrations, respectively. Standard pharmacokinetic parameter and partial AUCs (1-2 hours, 1-3 hours, 1-4 hours, 2-4 hours, and 1-6 hours) after oral administration of midazolam were calculated by noncompartmental analysis using WinNonlin 5.2 (Pharsight Corp., Mountain View, CA, USA). Midazolam concentrations were expressed per mg dose of midazolam. Non-linear regressions were performed using Prism 5.03 (GraphPad Software Inc., La Jolla, CA, USA). From the following equations [7] we derived a formula to calculate the metabolic clearance of midazolam to 1'-hydroxymidazolam using a partial AUC of midazolam.

Clearance is defined as the removal of substance from blood, expressed as volume per time.

C CL C V C V k A k dt dAe z z ⋅ = ⋅ ⋅ = ⋅ ⋅ = ⋅ = λ 10 10
(1) Ae (total eliminated drug), k (elimination rate constant first order), A (amount of drug), V (volume of distribution), C (concentration), λ (reaction rate constant first order), CL (clearance), t (time after medication)

The total eliminated drug is consistent with the integral of equation 1

∫ ∞ ∞ ⋅ = ⋅ = 0 AUC CL dt C CL Ae (2)
Ae ∞ (total eliminated drug until time point of infinity), AUC (area under the curve) which is also equal to systemically available dose (F x D)

D F AUC CL Ae ⋅ = ⋅ = ∞ (3) F (bioavailability), D (dose) D CL F AUC ⋅ = (4)
Clearance and AUC are inversely related, and almost no midazolam is excreted unchanged in urine. Hence, clearance can be replaced by metabolic clearance. By means of non-linear regression of various partial AUCs and metabolic clearance of midazolam to 1'hydroxymidazolam (Prism 5.03) the factor A was calculated:

met CL A partialAUC = (5)
Using the best fit (highest r 2 ) the prediction of the metabolic clearance of midazolam to 1'hydroxymidazolam from this partial AUC is done by: In order to verify the statistical significance of this predictive method the 95% confidence interval of the differences (%) between predicted metabolic clearance and measured metabolic clearance of midazolam to 1'-hydroxymidazolam was constructed. Precision and bias were assessed by the Bland-Altman method [8].

partialAUC A CL predicted met = - (6)

Results

All partial AUCs / mg midazolam of 1-2 hours, 1-3 hours, 1-4 hours, 2-4 hours and 1-6 hours after oral medication correlated with metabolic clearance of midazolam to 1'hydroxymidazolam. The full plasma concentration time profile of midazolam is shown in Figure 1, correlation results in Table 1. The correlation of partial AUC from 2-4 hours with the measured metabolic clearance of midazolam to 1'-hydroxymidazolam showed the highest r 2 -value (r 2 = 0.9816; Figure 2). This partial AUC comprises of four blood samples at 2, 2.5, 3 and 4 hours after oral midazolam administration. The use of a further reduced number of plasma samples to calculate partial AUC results in weaker correlations (partial AUC of 2 -4 hr with three blood samples at 2, 3, 4 hr: r 2 = 0.9062, partial AUC of 2 -4 hr with two blood samples at 2, 4 hr: r 2 = 0.8795).

Related to this non-linear regression the factor was calculated as A = 5656. Therefore, the following formula predicts the metabolic clearance of midazolam to 1'-hydroxymidazolam and CYP3A activity, respectively:

4 2 5656 - - = AUC CL predicted met (7)
The midazolam concentrations to calculate partial AUC have to be expressed per mg dose of midazolam.

There is a strong relationship between the measured and predicted metabolic clearance of midazolam to 1'-hydroxymidazolam. Using linear regression, regression coefficient was 0.98, p<0.0001. The 95% confidence interval of the differences between predicted metabolic F o r P e e r R e v i e w 9 clearance and measured metabolic clearance of midazolam to 1'-hydroxymidazolam [-0.97 % to +13.2 %] showed no significant difference. There was good precision and low bias of the predicted metabolic clearance of midazolam to 1'-hydroxymidazolam (Figure 3). In contrast the 95% confidence interval of the differences between predicted metabolite clearance and measured metabolic clearance of midazolam to 1'-hydroxymidazolam using this partial AUC and minor plasma samples showed a significant difference (95% confidence interval using three blood samples (2, 3, 4 hr):[+18.11% to +31.68%], 95% confidence interval using two blood samples (2, 4 hr): [+13.33% to +28.71%] ).

Discussion

The aim of this investigation was to find a useful strategy which predicts CYP3A activity in an easy and reliable way and facilitates the clinical application of CYP3A phenotyping and the determination of the time course of drug drug interactions. LSS has already been proven to be an effective tool in monitoring the safety and efficacy of chemotherapeutic agents by minimising the number of blood samples necessary to evaluate AUC [9]. It was first introduced in 1987 in order to minimise the number of plasma samples for measuring AUC of vinblastine. With only two time points, Ratain and Vogelzang were able to estimate total AUC using stepwise multiple regression analysis [10]. Since then various LSSs have been investigated for many drugs in order to simplify sampling procedures and minimise costs.

Regarding midazolam and the activity of CYP3A Rogers et al. found that neither single plasma concentrations of the metabolite 1-hydroxy-midazolam nor the ratio of 1-hydroxymidazolam and midazolam can be used for predicting midazolam clearance in healthy subjects [11]. Another study was performed to investigate limitations of using a single postdose midazolam concentration to predict CYP3A-mediated drug-drug interactions. The purpose was to assess the ability of one single midazolam concentration for predicting midazolam AUC in the presence and absence of CYP3A modulation by Gingko biloba The study showed that optimal midazolam sampling times to predict AUC were different before or after Gingko biloba [12]. This approach is not useful for prospective clinical trials where CYP3A activity is to be determined. Another strategy is the use of saliva for CYP3A phenotyping using midazolam [10]. This method however requires numerous samples over at least 6 hours and a very sensitive analytical method due to the very low concentrations in saliva [13].

Midazolam pharmacokinetics is well known und understood. Primary metabolic pathway is the formation of 1'-hydroxymidazolam via CYP3A which is subsequently conjugated and excreted in urine and accounts for >70% of the oral dose administered [4]. Hence, quantification of this pathway by the metabolic clearance of midazolam to 1'hydroxymidazolam is the most suitable parameter for CYP3A activity. We investigated the correlations between various partial midazolam AUCs and metabolic clearance of midazolam to 1'-hydroxymidazolam in order to find a reliable method to predict midazolam metabolic clearance. When midazolam is administered as an oral solution the best results were obtained using the midazolam partial AUC from 2 to 4 hours comprising of four plasma samples at 2, 2.5, 3 and 4 hr after oral administration of midazolam. Resorption and distribution is fast after an oral solution and seems not to be influencing the prediction of CYP3A activity when using plasma samples from 2-4 hours after midazomal administration. The prediction of the metabolic clearance of midazolam to 1'-hydroxymidazolam is only valid for the study conditions described above and for healthy volunteers without any conditions which could modify the absorption, distribution, metabolism or excretion of drugs. However, this method might also be applied to patients with limited metabolism caused by liver diseases in order to identify drug-drug interactions. The alteration of CYP3A activity by the addition of a drug should also be valid in these patients with the limitation that the prediction of the metabolic clearance might not be accurate. General prediction of CYP3A-activity has to comprise a full range of metabolic activity, including induction, inhibition and normal state of enzyme activity. Although this LSS solely is derived from induced and normal CYP3A activity, it is clear from Figure 2 that inhibition of CYP3A activity will result in high partial AUCs and very low metabolic clearance approaching zero when there is no metabolic activity.

Conclusion

We propose a new LSS to predict midazolam metabolic clearance to 1'-hydroxymidazolam and thus CYP3A activity using a full pharmacokinetic dataset from an interaction study. The determination of the partial AUC from 2-4 hours after oral administration of midazolam predicts CYP3A4 activity with high precision and will enable the assessment of the time course of drug drug interactions and the clinical application of CYP3A4 phenotyping.

F

RESULTS

The midazolam partial AUC from 2-4 hours after oral administration correlated best with metabolic clearance of midazolam to 1'-hydroxymidazolam with r 2 = 0.9816. This partial AUC comprised of four midazolam concentrations at 2, 2.5, 3 and 4 hours after oral administration of a midazolam solution. The 95% confidence interval of the differences between predicted metabolic clearance and measured metabolic clearance of midazolam to 1'- To determine the activity of CYP3A midazolam is often used as a probe drug for this enzyme [2]. Midazolam belongs to the benzodiazepines family and is frequently used for anaesthetic induction, narcotic premedication and sedation of patients prior to surgery or other invasive medical procedures. It is extensively metabolised by CYP3A to its primary metabolite, 1'hydroxymidazolam and to minor metabolites, 4'-hydroxymidazolam and 1, 4'dihydroxymidazolam. Only 1 % of midazolam is eliminated unchanged in urine. Main urinary metabolite is 1'-hydroxymidazolamglucuronide, which amounts to 63-80 % of the administered dose within 24 hours. Conjugates of 4'-hydroxymidazolam and 1',4'dihydroxymidazolam amount to 3 % and 1 % in urine [3]. Orally administered midazolam is used to assess the total -intestinal and hepatic -CYP3A activity.

Usually, CYP3A activity is tested before and during treatment with a potential inducer or inhibitor. To assess the time course of inhibition or induction frequent midazolam testing would be required. The most predictive and reliable parameter to assess CYP3A activity is the metabolic clearance to 1'-hydroxymidazolam [4]. Therefore, a full pharmacokinetic profile (AUC 0-∞ ) and the urinary excretion of 1'-hydroxymidazolam and its glucuronide over at least 24 hours after single dose administration are required. Hence the use of midazolam as a probe We here propose a new limited sampling strategy to predict the midazolam metabolic clearance to 1'-hydroxymidazolam and subsequently CYP3A-activity in an easy and reliable way using much less plasma samples and no urine sampling. Limited sampling is a useful strategy to simplify proceedings and reduce costs and labour. This is relevant for the clinical application of CYP3A phenotyping.

Methods

The and urine were quantified by high performance liquid chromatography coupled to tandem mass spectrometry after modification of a published method [5] as described elsewhere [6].

The LC/MS/MS system (Thermo Fisher, Dreieich, Germany) consisted of a Surveyor Plus HPLC System and a triple stage quadrupole mass spectrometer (TSQ 7000 with API-2 ion source and performance kit). For quantification of the drugs a Phenomenex Max RP column (150 x 2 mm, 4 µm, Phenomenex, Aschaffenburg, Germany) with integrated guard column was used at 40°C for chromatographic separation. The bioanalytical method was validated according to the recommendations of the FDA in the range of 0.61-460 nmol/L for midazolam and 0.59-439 nmol/L for 1'-hydroxymidazolam in plasma and 1.53-153 nmol/L for midazolam and 73.2-7315 nmol/L for 1'-hydroxymidazolam in urine. Lower limits of quantification for midazolam and 1'-hydroxymidazolam in plasma were 0.61 and 0.59 nmol/l, respectively, (with accuracy/precision of -4.2%/5.0% and +11.6%/4.6%, respectively); lower limit of quantification in urine was 1.53 nmol/l for midazolam and 72.2 nmol/l for 1'dydroxymidazolam (accuracy/precision of +8.0%/7.9% and +3.8%/7.1%, respectively).

Metabolic clearance of midazolam to 1'-hydroxymidazolam was calculated as the amount of total (conjugated + unconjugated) 1'-hydroxymidazolam excreted in urine (0-24 h) divided by midazolam AUC 0-∞ .

In order to predict metabolic clearance of midazolam to 1'-hydroxymidazolam various partial midazolam AUCs were calculated and correlated with the metabolic clearance of midazolam to 1'-hydroxymidazolam. Stepwise multiple regressions were used to identify optimal time points and intervals of midazolam plasma concentrations, respectively. Standard pharmacokinetic parameter and partial AUCs (1-2 hours, 1-3 hours, 1-4 hours, 2-4 hours, and 1-6 hours) after oral administration of midazolam were calculated by noncompartmental analysis using WinNonlin 5.2 (Pharsight Corp., Mountain View, CA, USA). Midazolam concentrations were expressed per mg dose of midazolam. Non-linear regressions were performed using Prism 5.03 (GraphPad Software Inc., La Jolla, CA, USA). From the following equations [7] we derived a formula to calculate the metabolic clearance of midazolam to 1'-hydroxymidazolam using a partial AUC of midazolam.

Clearance is defined as the removal of substance from blood, expressed as volume per time.

C CL C V C V k A k dt dAe z z ⋅ = ⋅ ⋅ = ⋅ ⋅ = ⋅ = λ 10 10 (1)
Ae (total eliminated drug), k (elimination rate constant first order), A (amount of drug), V (volume of distribution), C (concentration), λ (reaction rate constant first order), CL (clearance), t (time after medication)

The total eliminated drug is consistent with the integral of equation 1

∫ ∞ ∞ ⋅ = ⋅ = 0 AUC CL dt C CL Ae (2)
Ae ∞ (total eliminated drug until time point of infinity), AUC (area under the curve) which is also equal to systemically available dose (F x D)

D F AUC CL Ae ⋅ = ⋅ = ∞ (3) F (bioavailability), D (dose) D CL F AUC ⋅ = (4)
Clearance and AUC are inversely related, and almost no midazolam is excreted unchanged in urine. Hence, clearance can be replaced by metabolic clearance. By means of non-linear regression of various partial AUCs and metabolic clearance of midazolam to 1'hydroxymidazolam (Prism 5.03) the factor A was calculated:

met CL A partialAUC = (5)
Using the best fit (highest r 2 ) the prediction of the metabolic clearance of midazolam to 1'hydroxymidazolam from this partial AUC is done by: In order to verify the statistical significance of this predictive method the 95% confidence interval of the differences (%) between predicted metabolic clearance and measured metabolic clearance of midazolam to 1'-hydroxymidazolam was constructed. Precision and bias were assessed by the Bland-Altman method [8].

partialAUC A CL predicted met = - (6)

Results

All partial AUCs / mg midazolam of 1-2 hours, 1-3 hours, 1-4 hours, 2-4 hours and 1-6 hours after oral medication correlated with metabolic clearance of midazolam to 1'hydroxymidazolam. The full plasma concentration time profile of midazolam is shown in Figure 1, correlation results in Table 1. The correlation of partial AUC from 2-4 hours with the measured metabolic clearance of midazolam to 1'-hydroxymidazolam showed the highest r 2 -value (r 2 = 0.9816; Figure 2). This partial AUC comprises of four blood samples at 2, 2.5, 3 and 4 hours after oral midazolam administration. The use of a further reduced number of plasma samples to calculate partial AUC results in weaker correlations (partial AUC of 2 -4 hr with three blood samples at 2, 3, 4 hr: r 2 = 0.9062, partial AUC of 2 -4 hr with two blood samples at 2, 4 hr: r 2 = 0.8795).

Related to this non-linear regression the factor was calculated as A = 5656. Therefore, the following formula predicts the metabolic clearance of midazolam to 1'-hydroxymidazolam and CYP3A activity, respectively: (7) The midazolam concentrations to calculate partial AUC have to be expressed per mg dose of midazolam.

There is a strong relationship between the measured and predicted metabolic clearance of midazolam to 1'-hydroxymidazolam. Using linear regression, regression coefficient was 0.98, p<0.0001. The 95% confidence interval of the differences between predicted metabolic 2 %] showed no significant difference. There was good precision and low bias of the predicted metabolic clearance of midazolam to 1'-hydroxymidazolam (Figure 3). In contrast the 95% confidence interval of the differences between predicted metabolite clearance and measured metabolic clearance of midazolam to 1'-hydroxymidazolam using this partial AUC and minor plasma samples showed a significant difference (95% confidence interval using three blood samples (2, 3, 4 hr):[+18.11% to +31.68%], 95% confidence interval using two blood samples (2, 4 hr): [+13.33% to +28.71%] ).

Discussion

The aim of this investigation was to find a useful strategy which predicts CYP3A activity in an easy and reliable way and facilitates the clinical application of CYP3A phenotyping and the determination of the time course of drug drug interactions. LSS has already been proven to be an effective tool in monitoring the safety and efficacy of chemotherapeutic agents by minimising the number of blood samples necessary to evaluate AUC [9]. It was first introduced in 1987 in order to minimise the number of plasma samples for measuring AUC of vinblastine. With only two time points, Ratain and Vogelzang were able to estimate total AUC using stepwise multiple regression analysis [10]. Since then various LSSs have been investigated for many drugs in order to simplify sampling procedures and minimise costs.

Regarding midazolam and the activity of CYP3A Rogers et al. found that neither single plasma concentrations of the metabolite 1-hydroxy-midazolam nor the ratio of 1-hydroxymidazolam and midazolam can be used for predicting midazolam clearance in healthy subjects [11]. Another study was performed to investigate limitations of using a single postdose midazolam concentration to predict CYP3A-mediated drug-drug interactions. The purpose was to assess the ability of one single midazolam concentration for predicting midazolam AUC in the presence and absence of CYP3A modulation by Gingko biloba The study showed that optimal midazolam sampling times to predict AUC were different before or after Gingko biloba [12]. This approach is not useful for prospective clinical trials where CYP3A activity is to be determined. Another strategy is the use of saliva for CYP3A phenotyping using midazolam [10]. This method however requires numerous samples over at least 6 hours and a very sensitive analytical method due to the very low concentrations in saliva [13].

Midazolam pharmacokinetics is well known und understood. Primary metabolic pathway is the formation of 1'-hydroxymidazolam via CYP3A which is subsequently conjugated and excreted in urine and accounts for >70% of the oral dose administered [4]. Hence, quantification of this pathway by the metabolic clearance of midazolam to 1'hydroxymidazolam is the most suitable parameter for CYP3A activity. We investigated the correlations between various partial midazolam AUCs and metabolic clearance of midazolam to 1'-hydroxymidazolam in order to find a reliable method to predict midazolam metabolic clearance. When midazolam is administered as an oral solution the best results were obtained using the midazolam partial AUC from 2 to 4 hours comprising of four plasma samples at 2, 2.5, 3 and 4 hr after oral administration of midazolam. Resorption and distribution is fast after an oral solution and seems not to be influencing the prediction of CYP3A activity when using plasma samples from 2-4 hours after midazomal administration. The prediction of the metabolic clearance of midazolam to 1'-hydroxymidazolam is only valid for the study conditions described above and for healthy volunteers without any conditions which could modify the absorption, distribution, metabolism or excretion of drugs. However, this method might also be applied to patients with limited metabolism caused by liver diseases in order to identify drug-drug interactions. The alteration of CYP3A activity by the addition of a drug should also be valid in these patients with the limitation that the prediction of the metabolic clearance might not be accurate. General prediction of CYP3A-activity has to comprise a full range of metabolic activity, including induction, inhibition and normal state of enzyme activity. Although this LSS solely is derived from induced and normal CYP3A activity, it is clear from Figure 2 that inhibition of CYP3A activity will result in high partial AUCs and very low metabolic clearance approaching zero when there is no metabolic activity.

Conclusion

We propose a new LSS to predict midazolam metabolic clearance to 1'-hydroxymidazolam and thus CYP3A activity using a full pharmacokinetic dataset from an interaction study. The determination of the partial AUC from 2-4 hours after oral administration of midazolam predicts CYP3A4 activity with high precision and will enable the assessment of the time course of drug drug interactions and the clinical application of CYP3A4 phenotyping.

F

  the time course of CYP3A activity is very limited. A useful strategy for the clinical practice of CYP3A4 phenotyping might be the reduction of the number of plasma samples with still accurate determination of CYP3A activity. It will then be possible to obtain CYP3A activity over many days and assess the time course of drug drug interactions.

11

 11 Using four plasma samples to determine partial AUC instead of one single plasma concentration minimises errors in prediction of CYP3A activity due to imprecisions of the analytical measurements.

Figure 1 :Figure 2 :Figure 3 : 2 PURPOSE

 1232 Figure 1: Mean ± SD (n=12) plasma concentration-time course of midazolam after oral

  the time course of CYP3A activity is very limited. A useful strategy for the clinical practice of CYP3A4 phenotyping might be the reduction of the number of plasma samples with still accurate determination of CYP3A activity. It will then be possible to obtain CYP3A activity over many days and assess the time course of drug drug interactions.

  development of this LSS is based on data of an in vivo drug-drug interaction study carried out at the clinical research unit of the Department of Clinical Pharmacology and Pharmacoepidemiology, University of Heidelberg. In this study twelve participants were included. The protocol was approved by the competent authority (BfArM, Germany) and the Ethics Committee of the University of Heidelberg, written informed consent was obtained from each subject prior to study-related procedures. On the first occasion they received midazolam in order to determine the baseline metabolic clearance of midazolam to 1'hydroxymidazolam. Thereafter efavirenz (400 mg p.o. once daily) was administered over 14 days to study the inducing effect of efavirenz on CYP3A. Midazolam was given orally on day 1 and 14 of this period. After an overnight fast 3 mg midazolam (Dormicum ® Roche 5mg/5ml, solution for injection or infusion) were administered in 100 ml tap water. 4 hours thereafter a standard meal was provided for each subject. Blood samples were taken after midazolam at 0, 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 10 hours and urine was collected completely over 24 hours. Concentrations of midazolam and 1'-hydroxymidazolam in plasma

  metabolic clearance of midazolam to 1'-hydroxymidazolam [-0.97 % to +13.

11 Using

 11 four plasma samples to determine partial AUC instead of one single plasma concentration minimises errors in prediction of CYP3A activity due to imprecisions of the analytical measurements.

Figure 1 :Figure 2 :Figure 3 :

 123 Figure 1: Mean ± SD (n=12) plasma concentration-time course of midazolam after oral

Figure 1

 1 Figure 1:

Table 1 :

 1 Non linear regression of different midazolam partial AUCs / mg midazolam and measured metabolic clearance of midazolam to 1'-hydroxymidazolam (r 2 -coefficient of correlation).

	o r
	P
	e
	e r
	R
	e
	v i e
	w

  The determination of the partial AUC using four plasma samples from 2-4 hours after oral administration of midazolam as a solution is proposed to be an easy and reliable CYP3A phenotyping measure which of course needs to be validated in prospective clinical trials.

	Introduction
	Cytochromes P (CYPs) 450 represent a multifaceted family of heme-containing enzymes
	belonging to the group of monooxygenases accountable for phase-1-metabolism. Mammalian
	cytochromes are membrane proteins taking an active part in biosynthesis and metabolism of
	many physiological active substances. Moreover these enzymes are capable of catalysing
	biotransformation and metabolism of substances of foreign origin like drugs and toxins [1].
	Cytochrome P 450 3A (CYP3A) is the most abundant enzyme of CYPs in the liver and the
	F o r gut, it metabolises approximately 50% of currently available drugs and accounts for 30% of F the total CYP enzymes in hepatic microsomes. A large number of drugs interfere with o r CYP3A as they may act as inducers, substrates or inhibitors of this enzyme.
	P P
	e e
	e r e r
	R R
	e e
	v i e v i e
	w w
	hydroxymidazolam was [-0.97 to +13.2].
	CONCLUSION
	3

Table 1 :

 1 ., et al., Pharmacokinetics of intravenous and oral midazolam in plasma and saliva in humans: usefulness of saliva as matrix for CYP3A phenotyping. Br J Clin Pharmacol, 2008. 66(4): p. 473-84. Non linear regression of different midazolam partial AUCs / mg midazolam and measured metabolic clearance of midazolam to 1'-hydroxymidazolam (r 2 -coefficient of correlation).

	o r
	P
	e
	e r
	R
	e
	v i e
	w

European Journal of Clinical Pharmacology