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Development of Novel Immunotherapeutics 155 Figure 3 The gap between preclinical models and clinical setting for cancer vaccine development. of technology facing significant hurdles in translation from preclinical to clinical stage results from the fact that cancer vaccines are based on defined antigens. Such vaccine candidates encompass a variety of vectors (polypeptides, cells, microbes, recombinant DNA), immunological information (epitopes) corresponding to the antigenic makeup of tumor cells, with the aim to instruct one’s cells to recognize and react against the malignant process. Despite the fact that it has been recognized that the immune systems in rodent and humans, for example, operate in quite similar fashion, the sequence of the overwhelming majority of defined epitopes within tumor antigens of interest is not identical between mouse and man. Even if new tools became available, such as HLA transgenic mice, overcoming significant differences in the T-cell repertoire in mouse and man, due to differential MHC restriction, the distinct antigen sequence poses formidable hurdles in face of deploying animal models for predicting the magnitude and quality of immune response to human epitopes. Most significantly, a human epitope translated into an investigational drug that encompasses even one amino acid difference at the TCR-engaging site relative to the rodent version is recognized as “nonself” by the immune system. This results into a more potent response that one would otherwise occur in the desired, ultimate setting. An overestimation of pharmacological potency of antigen-based vaccines by using preclinical modeling may occur, in addition, when differences between mouse and human sequences affect primary MHC anchor residues, rendering, for example, the mouse version irrelevant and the human one “nonself.” Thus, while there is some agreement that animal models can be employed to compare various methods of immunization and generally predict immunogenicity of an investigational compound in clinic, considerations outlined above and others (such as dosing) preclude accurate translation of magnitude and profile of immune response from preclinical models to clinical setting. Another aspect that has been even more difficult to model and translate to clinic was the impact of immune response on tumor regression or clinical
156 Bot and Obrocea outcome in general due to the fact that transplantable tumors are highly artificial. Models involving spontaneously arising tumors were developed—yet the nature of therapeutic targets in preclinical setting, with some exceptions, limited their value in relation to exploration of humanized investigational drugs. Overall, it was not yet possible to reproduce in a preclinical model the target expression environment within a human tumor—one reason was the complexity and scarcity of reliable information regarding the latter. A distinct feature that has been notoriously difficult to reproduce in preclinical setting has been the strict status of the immune system and immune repertoire from patients treated with multiple standard therapies (such as radio and/or chemotherapy). Since the MOA of active immunotherapies requires immune competence, this represents a major parameter. In fact, attempts to explore this question resulted in tantalizing observations consisting in additive or even synergistic effects between select chemotherapies (cyclophosphamide, paclitaxel, doxorubicin, cisplatin) and vaccination. Beyond the interesting scientific explanation having to do with a T-cell repertoire conditioning or interference with immune “breaks,” or the practical implications on designing innovative combination approaches in clinic, a major criticism remains: The effect of chemotherapies on immune system seems to be species specific so thorough clinical exploration is needed to elucidate whether these observations truly translate to man. Finally, a major difference between preclinical and clinical setting—that limits the translation of findings from the former to the latter—is the speciesspecific recognition and response to “biological response modifiers” or adjuvants in general. To be active, cancer vaccines rely on delivery of not only immunological information such as epitopes but, at the same time, of motifs or molecules that instruct the immune system to mount a response of a certain magnitude and profile, most often by influencing the innate immunity. Most recently, such molecules have been described as TLR ligands (CpG motifs binding to TLR9; ds or ssRNA binding to TLR3, 7, and/or 8; LPS analogues binding to TLR4; and even small molecules binding to TLR7 such as Imiquimode 1 ) that exert their function by activating receptor-positive DCs, NK cells, or other cells of the immune system. Since receptor distribution on cell subsets, the relative function of different subsets, and the fine specificity of receptors vary from species to species, it is not unexpected that, for example, the TLR9 ligands CpG motifs have a different optimal structure in relation to an effective innate immune stimulation in mouse and human. Therefore, preclinical modeling may not be entirely predictive relative to immune-stimulating properties of vaccine excipients. Altogether, these issues suffice to raise a fundamental question that is highly applicable to the preclinical modeling of active immunotherapies in cancer, but to a lesser extent to other therapeutic strategies relying on more direct mechanisms of action. Namely, one needs to acknowledge that preclinical evaluation of an investigational drug, like a cancer vaccine aimed to treat a
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Translational Medicine Series 6 Can
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TRANSLATIONAL MEDICINE SERIES 1. Pr
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Informa Healthcare USA, Inc. 52 Van
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Preface Throughout the last 20 year
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Preface vii Table 1 A Diverse Techn
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Preface . . . . v Contributors . .
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Contributors Pedro Alves Ludwig Ins
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1 Factoring in Antigen Processing i
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Factoring in Antigen Processing in
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2 Outlining the Gap Between Preclin
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Preclinical Models and Clinical Sit
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Table 1 Examples of Preclinical Act
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56 Srinivasan disease agents. Garda
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60 Srinivasan The above-mentioned a
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62 Srinivasan (69). In another stud
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64 Srinivasan patients with prostat
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66 Srinivasan 33. Salgia R, Lynch T
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84 Luptrawan et al. Dendritic cells
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86 Luptrawan et al. of cancer cells
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88 Luptrawan et al. NK cells can ki
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90 Luptrawan et al. Figure 2 Repres
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92 Luptrawan et al. state (50). Pat
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94 Luptrawan et al. ability of TIL
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98 Luptrawan et al. Figure 6 Tumor
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Table 1 Summary of results of phase
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102 Luptrawan et al. Table 1 Summar
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104 Luptrawan et al. antigens (84,8
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106 Luptrawan et al. 30. Ranges GE,
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206 Index Antigenic peptides degrad
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210 Index Freund’s adjuvants. See
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212 Index Langerhans cells, 133, 13
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214 Index [Peptide vaccines] invest
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216 Index TCRL. See TCR-like immuno
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Oncology and Immunology about the b
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