Stewart D J, Raaphorst G P, Yau J, Beaubien A R
Ottawa Regional Cancer Centre-Civic Division, Ontario, Canada.
Invest New Drugs. 1996;14(2):115-30. doi: 10.1007/BF00210782.
With chemotherapy, the in vitro and clinical dose-response curve is steep in some situations, but is relatively flat in others, possibly due to the mechanism by which tumors are resistant to chemotherapy. For tumors with resistance due to factors that actively decrease chemotherapy efficacy (e.g., p-glycoprotein, glutathione, etc.), one would predict that high dose chemotherapy and therapy with some resistance modulating agents would increase therapeutic efficacy. Such "active" resistance would most likely generally arise from gene amplification or over expression, and would be characterized by a shoulder on the log response vs. dose curve, with eventual saturation of the protective mechanism. On the other hand, one would expect that high dose chemotherapy and most resistance modulating agents would be of little value for tumors with resistance due to defective apoptosis or due to a deficiency in or decreased drug affinity for a drug target, drug activating enzyme, drug active uptake system, or essential cofactor. Such "passive" resistance would most likely generally arise from gene down regulation, deletion, or mutation, and would probably be characterized by a relatively flat log response vs. dose curve, or by a curve in which a steep initial section is followed by a plateau, as target, etc., is saturated. (If response were plotted vs. log dose, then compared to the curve for a sensitive cell line, the curve for active resistance would be analogous to the pharmacodynamic curve seen with competitive antagonism [i.e., a sigmoid curve shifted to the right], and the curve for most types of passive resistance would be analogous to the pharmacodynamic curve seen with noncompetitive antagonism [i.e., a sigmoid curve with reduced maximal efficacy]. As such, one might also refer to active vs. passive resistance as competitive vs. noncompetitive resistance, respectively.) Many tumor types probably possess a combination of active and passive mechanisms of resistance. New in vivo strategies could be helpful in defining dose-response relationships, mechanisms of resistance, and targets for resistance modulation. Such in vivo studies would be conducted initially in animals, but might also be tested clinically if animal studies demonstrated them to be feasible and useful. These in vivo studies would be conducted by randomizing 5-25 subjects to one of 10-20 dose levels over a potentially useful therapeutic range. Nonlinear regression analysis would then be used to define the characteristics of a curve generated by plotting against dose the log percent tumor remaining after the first course of therapy. While this might offer insight into the nature of resistance mechanisms present initially, plotting further tumor shrinkage vs. dose-intensity vs. course number for each later treatment course (or plotting dose-intensity vs. time to tumor progression) might provide information on how tumors become increasingly resistant to drugs following treatment.
对于化疗,体外和临床剂量反应曲线在某些情况下很陡,但在其他情况下则相对平缓,这可能是由于肿瘤对化疗产生耐药性的机制所致。对于因某些因素(如P - 糖蛋白、谷胱甘肽等)而导致耐药性,从而积极降低化疗疗效的肿瘤,人们可以预测高剂量化疗以及使用一些耐药性调节剂进行治疗会提高治疗效果。这种“主动”耐药性很可能通常源于基因扩增或过度表达,其特征是对数反应与剂量曲线出现一个平台期,最终保护机制达到饱和。另一方面,对于因凋亡缺陷、药物靶点、药物激活酶、药物主动摄取系统或必需辅助因子缺乏或药物亲和力降低而产生耐药性的肿瘤,人们预计高剂量化疗和大多数耐药性调节剂作用不大。这种“被动”耐药性很可能通常源于基因下调、缺失或突变,其特征可能是对数反应与剂量曲线相对平缓,或者是一条初始陡峭随后出现平台期的曲线,因为靶点等达到饱和。(如果将反应与对数剂量作图,那么与敏感细胞系的曲线相比,主动耐药的曲线类似于竞争性拮抗作用时的药效学曲线[即S形曲线右移],而大多数类型的被动耐药曲线类似于非竞争性拮抗作用时的药效学曲线[即最大效应降低的S形曲线]。因此,人们也可以分别将主动耐药与被动耐药称为竞争性耐药与非竞争性耐药。)许多肿瘤类型可能同时具备主动和被动耐药机制。新的体内策略可能有助于确定剂量反应关系、耐药机制以及耐药调节靶点。此类体内研究最初将在动物中进行,但如果动物研究证明其可行且有用,也可能进行临床测试。这些体内研究将通过在一个潜在有效的治疗范围内将5 - 25名受试者随机分配到10 - 20个剂量水平之一来开展。然后使用非线性回归分析来确定通过将第一个疗程后残留肿瘤的对数百分比与剂量作图所生成曲线的特征。虽然这可能有助于深入了解最初存在的耐药机制的性质,但绘制每个后续治疗疗程中进一步的肿瘤缩小情况与剂量强度及疗程数的关系图(或绘制剂量强度与肿瘤进展时间的关系图)可能会提供有关肿瘤在治疗后如何对药物产生越来越强耐药性的信息。