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Development of Novel Immunotherapeutics 157 human disease, is intrinsically limited in its predictable value. Paradoxically, preclinical modeling using humanized investigational drugs may be even more artificial in certain cases, as opposed to using model reagents. For example, asking key questions on whether an immunization approach can “break tolerance” against a self-tumor antigen. Thus, it seems that the value of preclinical modeling in support of active immunotherapies needs to be revised. We propose that instead of being used as a framework to test the pharmacology of humanized investigational drugs and bridge mechanically the discovery/optimization with the clinical stage, preclinical exploration’s mission in the case of cancer vaccines is to clarify key questions on their applicability and optimization strategy prior to initiating and in conjunction with clinical evaluation. If we recognize that every preclinical model or setting is defined by a set of parameters (e.g., the nature and potency of immunization approach, the match between the vaccine composition and target antigen on the tumor, and the level of expression of the target antigen and host’s immune competency), then preclinical exploration using highly experimental reagents would be capable of defining the limits and opportunities associated with a therapeutic approach. The impact on clinical strategy is vast since that way we direct the subsequent clinical exploration in a manner that would maximize the likelihood of success and minimize the risk. To exemplify, if we idealize the parameters enumerated above (use of an extremely potent immunization approach that leverages response against a “nonself” antigen, use of a transplantable tumor that is highly immunogenic in animals that are immune competent), the resulting experimental setting would allow us to answer a key question: Is cancer vaccination in its most potent version effective enough to trigger objective tumor regression in bulky disease setting? This is not a trivial question since it has been recently showed that while adoptive T-cell therapy with large number of activated effector cells resulted in tumor regression in man, previous vaccines failed to show that—on the other hand their modest immunogenicity has been a great confounding factor. As shown in Figure 4, summarizing the conclusion from literature, it seems that Figure 4 Relevance of preclinical modeling to define optimal clinical indications for cancer vaccines.
158 Bot and Obrocea preclinical modeling is actually capable to provide key answers to this question and firmly define the limitations of applicability to cancer vaccines. Most attempts to immunize in a prophylactic setting against tumor challenge or spontaneous tumor formation were met by success, irrespective of the platform technology used—a strong argument in support of the fact that the needed threshold in terms of magnitude of immune response or stringency related to the profile of immunity were low. That is actually the strongest criticism against overinterpreting data sets from clinical studies generated across different vaccine platforms—many of them proved subsequently to be quite suboptimal—in an established tumor setting. Whether cancer vaccines “work” or “do not work” is a question that is highly dependent on both the vaccine platform as well as the clinical setting in which they are tested. For example, data sets generated in preclinical models comprising limited solid tumor burden, with potent vaccination approaches, strongly suggest that immunization alone can be a quite effective means to objectively impact tumor progression. Unfortunately, immunization alone rarely achieves objective regression in a bulky tumor setting despite an optimal antigenic match between tumor and vaccine and a competent immune system. In contrast, achieving a strong systemic response may result in elimination of circulating tumor cells and obliteration of metastatic lesions. This is a key feature that may in fact impact survival by blocking disease spread or relapse, mainly responsible for serious morbidity and mortality in cancer. Altogether, this exemplifies how preclinical exploration sheds light on the limits of active immunotherapy or cancer vaccines: They are more likely to succeed in limited disease setting or minimal residual disease in indications such as adjuvant or consolidation therapy. Conversely, this along with mechanistic studies strongly suggested that in order for vaccination “to work” in a bulky or advance disease setting, one needs to contemplate more complex approaches that encompass the immunogen as well as compounds that interfere with various immune breaks limiting the activity of immune effector cells within tumors. More recently, the conventional wisdom that chemotherapy unavoidably hampers active immunotherapy has been seriously challenged by numerous findings in idealized preclinical settings encompassing both autoimmunity and vaccination against tumor antigens. From a mechanistic standpoint, it became clear that the time window associated with recovery of the T-cell repertoire offers an opportunity to induce tolerance or conversely, repopulate the immune system with antigen-specific T cells by vaccination. Chemotherapies, novel small molecules, or biomolecules may positively affect the outcome of active immunotherapy at multiple levels (Fig. 5). All of that can be explored by employing preclinical models such as: 1. Amplifying the frequency of antigen-specific T cells by vaccination postlymphodepletion carried out by chemotherapy (with or without myeloablation);
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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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Oncology and Immunology about the b
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