**6. Conclusion**

Since 2007, following the EMEA suspension of the marketing authorization of viracept (nelfinavir mesylate), genotoxic impurities have become a common issue for health concerns. Thus, regulatory agencies have made several attempts to construct a systematic method for controlling and analyzing GIs. However, several points must be considered for achieving a general view on the regulation of GIs.

One of the main problems is the very conservative limit regulated by agencies (1.5 µg/day). Bercu *et al.* (2009) calculated the permissible daily exposure (PDE) for EMS, which was the first GI of concern in 2007, as 0.104 mg/day. This value was found to be about 70-fold higher than the TTC level of 1.5 µg/day currently applied to EMS based on the generic linear back extrapolation model for genotoxins acting via non-threshold mechanisms. Other literatures highlighted this conservative limit as well (Gocke *et al.*, 2009b; Elder *et al.*, 2010a; Snodin, 2010). In addition, Gocke *et al.* (2009b) reported that the accidental exposure of viracept patients did not result in an increased likelihood for adverse genotoxic, teratogenic or cancerogenic effects.

In addition to the challenge of setting a more pragmatic limit for GIs, the development of extremely sensitive and robust analytical methods that can adequately monitor GIs at very low levels is very difficult. Also, the pharmaceutical industry has no long-term experience in the use of these methodologies within the factory setting. Thus, analysts make attempts to

Genotoxic Impurities in Pharmaceuticals 409

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determine a way for analyzing various GIs by using unique robust methods as far as possible. In this way, simple HPLC/UV or GC/FID methods are usually performed at the first stage, while more complicated LC/MS or LC/MS/MS methods are used as alternatives (Dobo *et al.*, 2006; Elder *et al.*, 2008b; Liu *et al.*, 2010).

Teasdale *et al.* (2009) studied the formation of sulfonate esters as a mechanistic view, and showed that when a slight excess of base is present, there is no discernible reaction rate to form the sulfonate ester and no mechanistic pathway to their formation. From this point of view, the formation of GIs and suspicious substances in the API syntheses can be easily avoided, and therefore this is the preferred option (Robinson, 2010).

Finally, it can be mentioned that in such a situation, *in silico* approaches can prove to be a more effective solution in terms of time and cost for screening genotoxic compounds. As subjected by Luis and Valerio (2009), high-quality experimental data must be used. In addition, for non-genotoxic carcinogens, QSAR studies can provide a better understanding about the mechanism of carcinogenesis of these compounds. The in silico methods used in agencies have not been specified yet; however, by overcoming the limits these can become an innate part of regulatory systems.

### **7. Acknowledgment**

This work is dedicated to Professor Hassan Mohseni, Tabriz University of Medical Sciences, Tabriz, Iran, for his enduring efforts in training toxicology in Iran.

#### **8. References**


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**18** 

*USA* 

**Measurement Uncertainty in** 

*Clearview Statistical Consulting, Snohomish, WA,* 

**Its Estimation, Reporting and Interpretation** 

All measurements, regardless of their purpose, context or quality, possess uncertainty. No measurement is performed with absolute perfection since all are approximations. Uncertainty, however, does not mean there is anything wrong or inappropriate with the results. Uncertainty is simply a measure of the confidence we have in our best estimate and results from limitations in our technology, our methods, our standards and our limited understanding of the property being measured. [Drosg] Uncertainty is a fundamental property of the natural world in which we live and work. Moreover, no measurement is fully interpretable within a given context until the full process generating the result is understood. The general additive measurement function observed in equation 1 illustrates

Our measurement is an imperfect representation of the measurand due to bias and random error components. Bias may be corrected for when reliably determined with traceable controls. Random error, on the other hand, cannot be corrected for but can be minimized to an acceptable level. Figure 1 illustrates how these two contributors to uncertainty influence measurement results - where we have assumed a normal distribution. Bias is simply the difference between the mean and the reference value while random error, determined by the variance or standard deviation, defines the width of the distribution. Figure 1 also illustrates another important property of measurement - all results are random variables that arise from a specified distribution. As a result they have a fixed mean and variance from which confidence intervals can be determined – an useful metric for defining uncertainty. The fact that uncertainty exists in our measurements, however, should not alarm us. We simply need to understand it, acknowledge it, estimate it in a statistically valid way, report it and ensure

*Y* 

**1. Introduction** 

this basic limitation of all measurements:

where: Y = the measurement result µ = the true value of the measurand β = measurement error due to bias ε = random measurement error

that it is fit-for-purpose.

**Forensic Toxicology:** 

(1)

Rod G. Gullberg

Yu, Y., Huang, T., Yang, B., Liu, X. & Duan, G. (2007). Development of gas chromatography– mass spectrometry with microwave distillation and simultaneous solid-phase microextraction for rapid determination of volatile constituents in ginger. *Journal of Pharmaceutical and Biomedical Analysis*, Vol. 43, pp. 24-31.
