Surrogate matrix: opportunities & challenges for tissue sample analysis Review
Lead generation
Lead optimization Discovery Number of compounds Requirements for analytical confidence Figure 1. Tiered approach for tissue analysis.
parameters, Schmidt et al. also evaluated the method robustness for the determination of retinoids in tissue using HPLC-UV [16]. The detection wavelength, tem- perature, flow rate, mobile phase content and pH were varied within a set range and the influence of the vari- ables on the quantification, retention time, peak height and peak area were determined. The US FDA guidance for Bioanalytical Method
Validation (FDA BMV) [40] and the European Medi- cines Agency guideline on bioanalytical method vali- dation (EMA BMV) [41] are widely adapted for validat- ing plasma assays. The FDA BMV is currently being revised. The draft revision was published in 2013 [42] and was discussed at the Crystal City V confer- ence [43]. While many aspects of tissue method valida- tion are similar to plasma method validation, there are specific challenges for tissue analysis such as recovery and stability. Xue et al. provided an overview of the guidelines, workshops and white papers that are rel- evant to tissue method validation to some extent [38]. Van de Merbel provided an excellent discussion on the specific issues for endogenous compound quan- titation using a surrogate matrix approach [2]. In the literature, the most frequently cited guideline is FDA BMV [3–4,17,22,24–25,27–28], followed by the International Conference on Harmonization Guideline (ICH) Q2B and Q2 (R1) [11–12,44–45]. One group stated their vali- dation was in compliance with Société Française des Sciences et Techniques Pharmaceutiques (SFSTP) guidelines [7,46–49].
Method performance assessment After surrogate selection and method development, method performance needs to be evaluated before applying it in tissue analysis. In this section, we will examine key method parameters, including sensitivity, specificity, selectivity, interference, linearity, precision, accuracy, recovery, matrix effects and stability. For each parameter, we will detail the general approaches
future science group
and considerations for performance assessment. Based on the tiered approach, not all method parameters are to be evaluated. We hope the discussion will facilitate the making of informed decision to strike a proper bal- ance between the method qualification efforts and the intended use of the data.
Sensitivity For sensitivity evaluations, many reported both LOD and LLOQ [7,11–12,14,29]. Some evaluate the LLOQ in surrogate only [4,8,11,50]. But many others use the authentic tissue to verify the assay sensitivity [17,29]. Such practice has proved to be beneficial because there were cases that the assay LLOQ had to be raised due to matrix effects from the authentic tissue [6]. The accep- tance criteria used for evaluating LOD and LLOQ are similar among the researchers, which is minimal S/N ratio of 3 for LOD [10,14] and precision and accuracy within 20% for LLOQ. In addition, many people also require S/N at least 5 or 10 for LLOQ [11,17,27,29].
Specificity, selectivity & interference It appears many researchers use the specificity and selectivity interchangeably or use one term when they mean the other or both. To a large degree, this ambi- guity is
introduced by the FDA BMV, which lists
selectivity as one of the headings but not specificity. However, in a different section the BMV does also state that specificity should be established [40]. In our opinion, for LC–MS/MS assay, specificity evaluation is performed to ensure that blank samples are free of interference while selectivity evaluation is performed to ensure the quantified results are precise and accu- rate in the presence of components that exists in study samples (real samples), such as metabolites, impurities, degradants and matrix components. In the literature, most researchers examine assay specificity. By con- trast, only a few researchers evaluate selectivity. The samples used for selectivity evaluations vary, ranging
www.future-science.com 2425
Candidate selection
Non- clinical
Development
Clinical
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154