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Mathematical Biosciences 000 (2014) 1–5
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Network biology in development of monoclonal antibody therapeutics Ayse Meric Ovacik∗ Merck Research Laboratories, 901 S. California Avenue, Palo Alto, CA 94304, USA
a r t i c l e
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Article history: Received 1 August 2014 Accepted 3 September 2014 Available online xxx Keywords: Monoclonal antibodies Drug development Network biology Transcriptomics Proteomics Systems biology
a b s t r a c t Monoclonal antibodies (mAbs) are large glycoproteins that recognize and remove/neutralize a specific target. Inflammation and inflammatory diseases are often treated with mAb-based therapeutics. Mathematical modeling is widely used in development of mAbs. Bioinformatics and structural modeling is used for humanization of mAbs and PK/PD modeling is extensively used in preclinical and clinical development. The objective of this commentary is to introduce systems biology-based modeling that can accelerate and improve development of mAbs. © 2014 Elsevier Inc. All rights reserved.
1. Introduction Antibodies, also called immunoglobulins, are large glycoproteins that recognize and remove/neutralize a specific target called antigen. Antigens can be foreign ‘objects’, such as bacteria and viruses, or the body’s own molecules, such as cytokines, interleukins, and receptors. All antibodies are composed of two identical heavy chains and two identical light chains as a basic functional monomer unit. The antigenbinding (target-binding) region of an antibody, referred to as the fragment antigen binding (Fab) region; it recognizes a specific part of an antigen. Part of the heavy chain of an antibody is referred to as the fragment crystallizable (Fc) region. Fc region characterizes subclasses of antibodies and enables binding to Fc receptors (FcR). FcR are located on a number of cells in the immune system, including B lymphocytes, dendritic cells, and natural killer cells. Antibodies bind to FcR only when they are attached to an antigen. Binding of antibodies to FcR activates biological processes for clearance of antigens or stimulates lysis of target cells through phagocytosis or antibody-dependent cellmediated cytotoxicity. Antibodies, their structure, subclasses, and function have extensively been studied [14,40,45,46]. An immune response against an antigen results in a collection of antibodies with different specificity and affinity [17]. This collection of antibodies (defined as polyclonal antibodies), targets the same antigen but via different binding sites (called epitopes) on the antigen. In contrast, monoclonal antibodies (mAbs) are not only specific for the same antigen, but also for the same epitope; they recognize the exact same part of an antigen. Produc-
∗
Tel.: +1 650 496 6454. E-mail address: [email protected]
tion of mAbs is possible due hybridoma technology [24]. This technology includes immunization of animals, mostly rodents, with an antigen and leads to activation of B cells that produce antibodies against that antigen. The antibody-producing B cells are then isolated and co-cultured with an immortal myeloma cell line to create hybridoma, a fusion of an antibody producing B-cell and a myeloma cell [28]. These hybridoma cells are subsequently cloned and produce identical mAb. Since mAbs are derived from a single progenitor cell, they are homogeneous with respect to isotype, epitope, affinity, and specificity. Immunization of mice with a human antigen results in a mAb that binds to that specific human antigen but has a mouse backbone. In fact, the first mAb approved for use in humans was a mouse mAb muromonoab-CD3 that targets human CD3 [18]. Administration of mouse mAbs to humans results in an immune response against these antibodies, which leads to their toxicity and limited efficacy in humans. In addition, these mAbs have a very short half-life due to weak interactions with human FcR [36]. This was the fate of muromonoabCD3 and other early mouse mAbs for use in humans [18,26]. Hence, a number of approaches have been developed to render animal mAbs less immunogenic. These approaches include development of chimeric, humanized, and human antibodies [2]. A chimeric antibody is composed of human constant regions and animal variable regions. In a humanized antibody, 90–95% of the antibody is human, and 5–10% is animal. Human antibodies are fully derived from human germline sequences. Development of mAbs for therapeutic use starts with selection of the target, which requires often requires extensive validation to understand the most effective way to modulate its activity. Candidate mAbs are generated using hybridoma technology [24]. If a human
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mAb does not bind to the mouse ortholog of the human target, a surrogate antibody is used to facilitate target validation and proof of concept studies. Selection of the lead for both surrogate and human mAbs requires employment of screening technologies and assays to identify desired favorable biochemical and biophysical properties [48]. While humanization, engineering of the Fc region, selection of a relevant species for preclinical safety studies, and exploratory PK/PD studies are carried on, manufacturability, selection of potential biomarkers, and clinical development plans are conducted. This process is described in detail in the literature [31,35,52,16]. Although guidelines and texts for development of mAbs are available, each target and its mAb are unique and require case-by-case approach. In recent years, mAbs have become essential in the treatment of inflammation and inflammatory diseases. Four of ten top-selling mAbs in 2012 were for treatment of chronic inflammatory conditions [20]. The mechanistic properties of mAb for the treatment of inflammation and inflammatory diseases include blocking cytokines (such as TNF-α ) and several interleukins (such as IL-12/23, IL-17, and IL-6) [25,55]. Quantitative pharmacology and model-based approaches are currently being applied in development of mAbs for the treatment of inflammation and inflammatory diseases; they include structural biology, bioinformatics/computational biology (humanization/chimerization efforts), and PK/PD modeling (preclinical and clinical development). For example, interspecies scaling of mAbs [29] is indispensable for determination of the first-in-human dose [33]. Moreover, indirect response modeling provides insights into determination of efficacious dose and dose escalations for clinical studies [44]. Approved mAb and ongoing clinical studies offer a foundation for development of new mAbs for the treatment of inflammation and inflammatory diseases. Quantitative models are emerging as a powerful and useful tool in drug development; thus, the use of these models and related methods have been studied and reviewed extensively [53,51,30,19]. Although an array of quantitative modeling approaches has been developed on biological systems for inflammation and inflammatory diseases, they were rarely used in a model-based drug development. These aforementioned modeling approaches commonly referred to as systems biology, include both theoretical as well as “big data”-based modeling. A thorough analysis of the gap between academic understanding and industry use of these models was published in 2011 [1]. In this publication, the causes of this gap are identified and a high-level framework to integrate systems biologybased models into drug development is presented. Five main types of systems biology-based models are described in this publication: heuristic, semi-mechanistic, mechanistic, network, and multi-scale systems pharmacology models. The focus of this commentary is to provide an insight into benefit of network biology-based models in development mAb for treatment of inflammation and inflammatory diseases.
2. Omics studies It is impossible to address network biology-based models without addressing their relations to omics studies. Omics studies is a generic term referring to the genome-scale data sets that are emerging from high-throughput tools and technologies [22]. Examples of omics studies include whole-genome sequencing data (genomics), microarraybased genome-wide expression profiles (transcriptomics), and largescale expression of proteins (proteomics). New specialized terms of omics studies are emerging as more scientific disciplines are interested in the systems biology such as pharmacogenomics or immunoproteomics. Although omics-based studies are widely implemented in understanding diseases and in mathematical models to predict clinical outcome, these studies are yet to be translated into drug development and clinical studies [32].
One modeling approach of omics studies is to use clustering and statistical methods to identify important genes related to a disease or different treatments for a disease. Koczan et al. showed that a subset of genes identified through transcriptomics data can identify patients with rheumatoid arthritis who respond to a treatment with anti-TNF-α mAb (etanercept) [23]. These important genes (or proteins) are then mapped onto known functional groups and interaction networks. Finally, the significance of enrichment of functional groups or interaction networks with the important genes is evaluated statistically. Calvano et al. analyzed changes in gene expression patterns in peripheral blood leukocytes in human subjects receiving a bacterial endotoxin as an immunostimulator [11]. The known genome-wide interaction network, retrieved from Ingenuity Pathway Analysis (www.ingenuity.com), was explored to identify significant functional groups in response to an inflammatory stimulus with bacterial endotoxin. This analysis revealed that response of peripheral blood leukocytes to inflammatory stimulus with bacterial endotoxin was mainly dysregulation of distribution in energy flow and modulation of translational mechanisms. However, understanding underlying mechanisms of such a biological phenomenon through transcriptomics studies is challenging due to inter- and intra-patient variability and variability of transcriptomics studies [42,49]. Reproducibility of transcriptomics studies can be monitored by measuring expression levels of the genes of interest by quantitative real-time PCR [23]. Inter- and intra-patient variability in omics studies is reduced with stringent statistical methods [50,32]. mAbs may exhibit complex, non-linear pharmacokinetics, with substantial inter- and intra-patient variability mostly due to changes in expression of the target [15]. Such variability is incorporated in PK/PD modeling if the covariates driving variability are identified. Typical exploratory covariates for PK analysis in clinical studies are weight, age, gender, and race of subjects. In addition to these typical covariates, disease-specific characteristics are also included in covariate analysis. For psoriasis, disease-specific covariates include duration of the disease, PASI score, diabetes, hypertension, and prior treatment with immunosuppressive or protein-based drugs [54]. (Psoriasis Area and Severity Index or PASI score is measured on selected skin regions where intensity of redness, thickness, and scaling of skin lesions is assessed; hence, it is a composite index reflecting the severity of the disease [4].) With the increasing knowledge and developing technologies, it is clear that variation in drug exposure and drug responses may emerge from genomic changes. Thus, identifying variability and clinical covariates should be based on clinical data obtained from a large population of patients and appropriate demographics [21]. Understanding the relationship between changes in gene expression at the level of genome in blood and exposure to mAb early in clinical development could facilitate the late stage clinical studies assessing efficacy. For example, significant changes in gene expression at the level of genome, e.g., group of important genes, may serve as an identifier for variability in exposure to mAb in blood and facilitate selection of patients. An example of potential utilization of omics data in clinical studies is integrating placebo effect observed in the treatment of psoriasis into selection of patients. Administration of placebo to some patients with psoriasis resulted in significant effect on PASI score in multiple clinical studies; thus, these patients are not candidates for treatment. The time-course of the placebo effect on PASI score was captured in an empirical function form with an indirect response model along with that observed following administration of ustekinumab and brodalumab [54,44]. Since placebo effect could only be described by an empirical function and not a mechanistic model, the predictions based on the final model were uncertain. On the other hand, correlation of omics data with placebo effect and the drug effect may enable reliable selection of patients who do not need treatment. Another modeling approach using genomics data is construction of a disease-specific network [8]. Nair et al. developed a protein–protein
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Fig. 1. Overview of mAb based drug development stages (adapted from [48]) with relevant types of mathematical modeling. Structural biology and PK/PD modeling are already well integrated into development of mAb. Systems biology-based modeling could be used in development of mAb from early nonclinical to clinical stages.
interaction inflammatory network specific for coronary artery disease [34]. In this network, hubs (components) important for coronary artery disease were identified. The identified important hubs were analyzed using cluego [9], which is a tool for visualization of functionally related genes displayed as a clustered network and chart. In 64 patients with coronary artery disease compared with 64 controls, transcriptional regulators of these important hubs were identified and their expression levels in whole blood were analyzed. The analysis revealed increased expression of the five hubs (IL-6, VEGFA, ILI1B, TNF, PTGS2) and the three transcriptional regulators (NFKB1, STAT3, and JUN). Similarly, a drug-specific network utilizing the omics data can be constructed. The identified genes and transcriptional regulators could facilitate determination of biomarkers, which could then be used for selection of patient population who will respond to treatment. There are two major challenges of inclusion of omics studies into clinical studies. The first is the design of clinical studies. The second is the lack of approaches to minimize variability and increase reproducibility in the omics studies with unbiased statistical techniques to provide a clear link between drug exposure and drug response. Availability of the large scale datasets that span the entire immunoproteome [41] could improve the understanding of response to treatment in inflammation and inflammatory diseases.
3. Network biology The interaction and collective behavior of certain sets of biological molecules that perform a specific task, such as protein synthesis, form functional units, identified as biological networks [3]. Network biology provides a framework to explore molecular complexity of particular diseases, including inflammation and inflammatory diseases. In addition, molecular relationships among distinct treatment outcomes, such as response to treatment, can be evaluated [7]. A challenge of network biology is to model quantitatively the dynamic properties of biological networks. Quantitative changes (increase or decrease) of the elements in a biological network following ‘an intervention’, i.e., administration of a drug, are compared to a control state. The end result is a collection of several snap-shots of
the same biological network from different treatment groups. Snapshots are then summarized and quantified using pathway activity level method [39]. In this method, changes in biological networks (snap-shots) are quantified based on omics data using a single metric for entire study. The same method successfully analyzed changes in gene expression in relevant biological networks following exposure to a toxic agent [38]. In pathway activity level method, an integrated approach is used, where transcriptomics data and network biology are integrated to characterize either a biological state or treatment groups. Integrating omics-based data with network biology identifies not only potential biomarkers but also possible toxicity pathways arising from the same pharmacological effects. Nevertheless, more effective method is required to incorporate omics studies into mechanistic understanding of drug effect. An early example of such an effort was developed by Foteinou et al., where an endotoxin-induced human inflammation PK/PD model was established by incorporating transcriptional responses [12]. The developed model was then used to predict clinical outcomes such as heart rate [13]. Selecting a relevant species for prediction of a first-in-human (FIH) dose is of utmost importance for development of mAb. Severe adverse events observed following administration of an anti-CD28 superagonistic mAb have highlighted the importance of nonclinical safety studies including selecting relevant species [33,5]. Gene and protein expression levels of the target and comparison of epitope for binding to mAb and pharmacological activity between animal species and human are all important factors for determining a relevant species for safety studies [5]. Equally complicated and comprehensive process is the search for translational biomarkers. Network biology-based modeling can help to determine a relevant species and translational biomarkers. For example, cross-species comparison of biological networks enables establishing whether a potential mechanism of action between the animal species and human is similar or different. Cross-species comparisons of certain biological entities (such as protein sequence, genome sequence) have been developed by evolutionary biology and comparative genomics [10]. One striking example is the protein sequence comparison of the CYP2A family of cytochrome P450 in mouse, rat, and human liver [47]. The rat enzyme varied significantly from the human and mouse enzyme. This difference in the protein sequence was translated into hepatotoxic
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metabolization of a substrate in rat whereas the same substrate was harmless in human and mouse [27]. Similar comparative analyses (yeast, rat, mouse, human and others) were performed across biological networks [37]. However, cross-species comparison of biological networks is not trivial as multiple features of biological networks contribute to their function. One such feature is network structure, which includes the relations between components of network and regulators and the functional information of the network components, e.g., protein sequence and protein structure. Combining multiple features of biological networks improves their cross-species comparison [37] Current network biology-based models have a major limitation: they are generated based on information obtained from literature mining or databases [6]. Therefore, network biology-based models may suffer from incompleteness or missing interactions. To some extent, using multiple database sources addresses these issues [43]. As our understanding of interactions in living systems improves, the network biology-based models will provide more informative framework for drug development. 4. Future directions Overview of stages for development of mAbs and related mathematical modeling approaches are depicted in Fig. 1. Structural biology and PK/PD modeling are essential tools for development of mAbs. There are initiatives to integrate systems biology-based modeling and omics data into preclinical and clinical studies. Such modeling could accelerate and improve development of new mAbs. Systems biologybased modeling is not yet well integrated into development of mAbs. In contrast, omics data with network biology can already be utilized for characterization and prioritization of candidates and identification for biomarkers. Paramount of information on inflammation and inflammatory disease from systems biology perspective is available. However, the main challenge is to translate systems biology modeling into quantitative (semi-) mechanistic information to facilitate development of mAbs. Acknowledgment The author acknowledges Gorazd Drozina, MD, PhD for reviewing this commentary. References [1] D. Abernethy, R. Altman, et al., Quantitative and Systems Pharmacology in the Post-genomic Era, in: R. Ward (Ed.), New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms, National Institute of Health 2011. [2] V. Ahmadzadeh, S. Farajnia, et al., Antibody humanization methods for development of therapeutic applications, Monoclon. Antib. Immunodiagn. Immunother. 33 (2) (2014) 67–73. [3] R. Albert, Scale-free networks in cell biology, J. Cell Sci. 118 (Pt 21) (2005) 4947– 4957. [4] D.M. Ashcroft, A.L. Wan Po, et al., Clinical measures of disease severity and outcome in psoriasis: a critical appraisal of their quality, Br. J. Dermatol. 141 (2) (1999) 185–191. [5] H. Attarwala, TGN1412: from discovery to disaster, J. Young Pharm. 2 (3) (2010) 332–336. [6] G.D. Bader, M.P. Cary, et al., Pathguide: a pathway resource list, Nucleic Acids Res. 34 (Database issue) (2006) D504–D506. [7] A.L. Barabasi, N. Gulbahce, et al., Network medicine: a network-based approach to human disease, Nat. Rev. Genet. 12 (1) (2011) 56–68. [8] M. Benson, R. Breitling, Network theory to understand microarray studies of complex diseases, Curr. Mol. Med. 6 (6) (2006) 695–701. [9] G. Bindea, B. Mlecnik, et al., ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks, Bioinformatics 25 (8) (2009) 1091–1093. [10] S.L. Burgess-Herbert, S.Y. Euling, Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs, Toxicol. Appl. Pharmacol. 271 (3) (2013) 372–385. [11] S.E. Calvano, W. Xiao, et al., A network-based analysis of systemic inflammation in humans, Nature 437 (7061) (2005) 1032–1037. [12] P.T. Foteinou, S.E. Calvano, et al., Modeling endotoxin-induced systemic inflammation using an indirect response approach, Math. Biosci. 217 (1) (2009) 27–42.
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Grauer, et al., Will immunogenicity limit the use, efficacy, and future development of therapeutic monoclonal antibodies?, Clin. Diagn. Lab. Immunol. 1 (4) (1994) 365–372. [27] B.G. Lake, Coumarin metabolism, toxicity and carcinogenicity: relevance for human risk assessment, Food Chem. Toxicol. 37 (4) (1999) 423–453. [28] E.A. Lerner, How to make a hybridoma, Yale J. Biol. Med. 54 (5) (1981) 387–402. [29] J. Ling, H. Zhou, et al., Interspecies scaling of therapeutic monoclonal antibodies: initial look, J. Clin. Pharmacol. 49 (12) (2009) 1382–1402. [30] D. Mager, Application of pharmacokinetic/pharmacodynamic modeling in the development of antibody-based therapeutics, in: M. Tabrizifard, G. Bornstein, S. Klakamp (Eds.), Development of Antibody-Based Therapeutics 2012. [31] M.A. Mascelli, H. Zhou, et al., Molecular, biologic, and pharmacokinetic properties of monoclonal antibodies: impact of these parameters on early clinical development, J. Clin. Pharmacol. 47 (5) (2007) 553–565. 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Androulakis, Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison, Toxicol. Appl. Pharmacol. 271 (3) (2010) 363–371. [38] M.A. Ovacik, B. Sen, et al., Pathway modeling of microarray data: a case study of pathway activity changes in the testis following in utero exposure to dibutyl phthalate (DBP), Toxicol. Appl. Pharmacol. 271 (3) (2010) 386–394. [39] M.A. Ovacik, S. Sukumaran, et al., Circadian signatures in rat liver: from gene expression to pathways, BMC Bioinform. 11 (2010) 540. [40] E.A. Padlan, Anatomy of the antibody molecule, Mol. Immunol. 31 (3) (1994) 169–217. [41] A.W. Purcell, J.J. Gorman, Immunoproteomics: mass spectrometry-based methods to study the targets of the immune response, Mol. Cell. Proteomics 3 (3) (2004) 193–208. [42] L. Pusztai, K.R. Hess, Clinical trial design for microarray predictive marker discovery and assessment, Ann. Oncol. 15 (12) (2004) 1731–1737. [43] V.K. Ramanan, L. 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Contents lists available at ScienceDirect
Mathematical Biosciences journal homepage: www.elsevier.com/locate/mbs
Network biology in development of monoclonal antibody therapeutics Ayse Meric Ovacik∗ Merck Research Laboratories, 901 S. California Avenue, Palo Alto, CA 94304, USA
a r t i c l e
i n f o
Article history: Received 1 August 2014 Accepted 3 September 2014 Available online xxx Keywords: Monoclonal antibodies Drug development Network biology Transcriptomics Proteomics Systems biology
a b s t r a c t Monoclonal antibodies (mAbs) are large glycoproteins that recognize and remove/neutralize a specific target. Inflammation and inflammatory diseases are often treated with mAb-based therapeutics. Mathematical modeling is widely used in development of mAbs. Bioinformatics and structural modeling is used for humanization of mAbs and PK/PD modeling is extensively used in preclinical and clinical development. The objective of this commentary is to introduce systems biology-based modeling that can accelerate and improve development of mAbs. © 2014 Elsevier Inc. All rights reserved.
1. Introduction Antibodies, also called immunoglobulins, are large glycoproteins that recognize and remove/neutralize a specific target called antigen. Antigens can be foreign ‘objects’, such as bacteria and viruses, or the body’s own molecules, such as cytokines, interleukins, and receptors. All antibodies are composed of two identical heavy chains and two identical light chains as a basic functional monomer unit. The antigenbinding (target-binding) region of an antibody, referred to as the fragment antigen binding (Fab) region; it recognizes a specific part of an antigen. Part of the heavy chain of an antibody is referred to as the fragment crystallizable (Fc) region. Fc region characterizes subclasses of antibodies and enables binding to Fc receptors (FcR). FcR are located on a number of cells in the immune system, including B lymphocytes, dendritic cells, and natural killer cells. Antibodies bind to FcR only when they are attached to an antigen. Binding of antibodies to FcR activates biological processes for clearance of antigens or stimulates lysis of target cells through phagocytosis or antibody-dependent cellmediated cytotoxicity. Antibodies, their structure, subclasses, and function have extensively been studied [14,40,45,46]. An immune response against an antigen results in a collection of antibodies with different specificity and affinity [17]. This collection of antibodies (defined as polyclonal antibodies), targets the same antigen but via different binding sites (called epitopes) on the antigen. In contrast, monoclonal antibodies (mAbs) are not only specific for the same antigen, but also for the same epitope; they recognize the exact same part of an antigen. Produc-
∗
Tel.: +1 650 496 6454. E-mail address: [email protected]
tion of mAbs is possible due hybridoma technology [24]. This technology includes immunization of animals, mostly rodents, with an antigen and leads to activation of B cells that produce antibodies against that antigen. The antibody-producing B cells are then isolated and co-cultured with an immortal myeloma cell line to create hybridoma, a fusion of an antibody producing B-cell and a myeloma cell [28]. These hybridoma cells are subsequently cloned and produce identical mAb. Since mAbs are derived from a single progenitor cell, they are homogeneous with respect to isotype, epitope, affinity, and specificity. Immunization of mice with a human antigen results in a mAb that binds to that specific human antigen but has a mouse backbone. In fact, the first mAb approved for use in humans was a mouse mAb muromonoab-CD3 that targets human CD3 [18]. Administration of mouse mAbs to humans results in an immune response against these antibodies, which leads to their toxicity and limited efficacy in humans. In addition, these mAbs have a very short half-life due to weak interactions with human FcR [36]. This was the fate of muromonoabCD3 and other early mouse mAbs for use in humans [18,26]. Hence, a number of approaches have been developed to render animal mAbs less immunogenic. These approaches include development of chimeric, humanized, and human antibodies [2]. A chimeric antibody is composed of human constant regions and animal variable regions. In a humanized antibody, 90–95% of the antibody is human, and 5–10% is animal. Human antibodies are fully derived from human germline sequences. Development of mAbs for therapeutic use starts with selection of the target, which requires often requires extensive validation to understand the most effective way to modulate its activity. Candidate mAbs are generated using hybridoma technology [24]. If a human
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mAb does not bind to the mouse ortholog of the human target, a surrogate antibody is used to facilitate target validation and proof of concept studies. Selection of the lead for both surrogate and human mAbs requires employment of screening technologies and assays to identify desired favorable biochemical and biophysical properties [48]. While humanization, engineering of the Fc region, selection of a relevant species for preclinical safety studies, and exploratory PK/PD studies are carried on, manufacturability, selection of potential biomarkers, and clinical development plans are conducted. This process is described in detail in the literature [31,35,52,16]. Although guidelines and texts for development of mAbs are available, each target and its mAb are unique and require case-by-case approach. In recent years, mAbs have become essential in the treatment of inflammation and inflammatory diseases. Four of ten top-selling mAbs in 2012 were for treatment of chronic inflammatory conditions [20]. The mechanistic properties of mAb for the treatment of inflammation and inflammatory diseases include blocking cytokines (such as TNF-α ) and several interleukins (such as IL-12/23, IL-17, and IL-6) [25,55]. Quantitative pharmacology and model-based approaches are currently being applied in development of mAbs for the treatment of inflammation and inflammatory diseases; they include structural biology, bioinformatics/computational biology (humanization/chimerization efforts), and PK/PD modeling (preclinical and clinical development). For example, interspecies scaling of mAbs [29] is indispensable for determination of the first-in-human dose [33]. Moreover, indirect response modeling provides insights into determination of efficacious dose and dose escalations for clinical studies [44]. Approved mAb and ongoing clinical studies offer a foundation for development of new mAbs for the treatment of inflammation and inflammatory diseases. Quantitative models are emerging as a powerful and useful tool in drug development; thus, the use of these models and related methods have been studied and reviewed extensively [53,51,30,19]. Although an array of quantitative modeling approaches has been developed on biological systems for inflammation and inflammatory diseases, they were rarely used in a model-based drug development. These aforementioned modeling approaches commonly referred to as systems biology, include both theoretical as well as “big data”-based modeling. A thorough analysis of the gap between academic understanding and industry use of these models was published in 2011 [1]. In this publication, the causes of this gap are identified and a high-level framework to integrate systems biologybased models into drug development is presented. Five main types of systems biology-based models are described in this publication: heuristic, semi-mechanistic, mechanistic, network, and multi-scale systems pharmacology models. The focus of this commentary is to provide an insight into benefit of network biology-based models in development mAb for treatment of inflammation and inflammatory diseases.
2. Omics studies It is impossible to address network biology-based models without addressing their relations to omics studies. Omics studies is a generic term referring to the genome-scale data sets that are emerging from high-throughput tools and technologies [22]. Examples of omics studies include whole-genome sequencing data (genomics), microarraybased genome-wide expression profiles (transcriptomics), and largescale expression of proteins (proteomics). New specialized terms of omics studies are emerging as more scientific disciplines are interested in the systems biology such as pharmacogenomics or immunoproteomics. Although omics-based studies are widely implemented in understanding diseases and in mathematical models to predict clinical outcome, these studies are yet to be translated into drug development and clinical studies [32].
One modeling approach of omics studies is to use clustering and statistical methods to identify important genes related to a disease or different treatments for a disease. Koczan et al. showed that a subset of genes identified through transcriptomics data can identify patients with rheumatoid arthritis who respond to a treatment with anti-TNF-α mAb (etanercept) [23]. These important genes (or proteins) are then mapped onto known functional groups and interaction networks. Finally, the significance of enrichment of functional groups or interaction networks with the important genes is evaluated statistically. Calvano et al. analyzed changes in gene expression patterns in peripheral blood leukocytes in human subjects receiving a bacterial endotoxin as an immunostimulator [11]. The known genome-wide interaction network, retrieved from Ingenuity Pathway Analysis (www.ingenuity.com), was explored to identify significant functional groups in response to an inflammatory stimulus with bacterial endotoxin. This analysis revealed that response of peripheral blood leukocytes to inflammatory stimulus with bacterial endotoxin was mainly dysregulation of distribution in energy flow and modulation of translational mechanisms. However, understanding underlying mechanisms of such a biological phenomenon through transcriptomics studies is challenging due to inter- and intra-patient variability and variability of transcriptomics studies [42,49]. Reproducibility of transcriptomics studies can be monitored by measuring expression levels of the genes of interest by quantitative real-time PCR [23]. Inter- and intra-patient variability in omics studies is reduced with stringent statistical methods [50,32]. mAbs may exhibit complex, non-linear pharmacokinetics, with substantial inter- and intra-patient variability mostly due to changes in expression of the target [15]. Such variability is incorporated in PK/PD modeling if the covariates driving variability are identified. Typical exploratory covariates for PK analysis in clinical studies are weight, age, gender, and race of subjects. In addition to these typical covariates, disease-specific characteristics are also included in covariate analysis. For psoriasis, disease-specific covariates include duration of the disease, PASI score, diabetes, hypertension, and prior treatment with immunosuppressive or protein-based drugs [54]. (Psoriasis Area and Severity Index or PASI score is measured on selected skin regions where intensity of redness, thickness, and scaling of skin lesions is assessed; hence, it is a composite index reflecting the severity of the disease [4].) With the increasing knowledge and developing technologies, it is clear that variation in drug exposure and drug responses may emerge from genomic changes. Thus, identifying variability and clinical covariates should be based on clinical data obtained from a large population of patients and appropriate demographics [21]. Understanding the relationship between changes in gene expression at the level of genome in blood and exposure to mAb early in clinical development could facilitate the late stage clinical studies assessing efficacy. For example, significant changes in gene expression at the level of genome, e.g., group of important genes, may serve as an identifier for variability in exposure to mAb in blood and facilitate selection of patients. An example of potential utilization of omics data in clinical studies is integrating placebo effect observed in the treatment of psoriasis into selection of patients. Administration of placebo to some patients with psoriasis resulted in significant effect on PASI score in multiple clinical studies; thus, these patients are not candidates for treatment. The time-course of the placebo effect on PASI score was captured in an empirical function form with an indirect response model along with that observed following administration of ustekinumab and brodalumab [54,44]. Since placebo effect could only be described by an empirical function and not a mechanistic model, the predictions based on the final model were uncertain. On the other hand, correlation of omics data with placebo effect and the drug effect may enable reliable selection of patients who do not need treatment. Another modeling approach using genomics data is construction of a disease-specific network [8]. Nair et al. developed a protein–protein
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Fig. 1. Overview of mAb based drug development stages (adapted from [48]) with relevant types of mathematical modeling. Structural biology and PK/PD modeling are already well integrated into development of mAb. Systems biology-based modeling could be used in development of mAb from early nonclinical to clinical stages.
interaction inflammatory network specific for coronary artery disease [34]. In this network, hubs (components) important for coronary artery disease were identified. The identified important hubs were analyzed using cluego [9], which is a tool for visualization of functionally related genes displayed as a clustered network and chart. In 64 patients with coronary artery disease compared with 64 controls, transcriptional regulators of these important hubs were identified and their expression levels in whole blood were analyzed. The analysis revealed increased expression of the five hubs (IL-6, VEGFA, ILI1B, TNF, PTGS2) and the three transcriptional regulators (NFKB1, STAT3, and JUN). Similarly, a drug-specific network utilizing the omics data can be constructed. The identified genes and transcriptional regulators could facilitate determination of biomarkers, which could then be used for selection of patient population who will respond to treatment. There are two major challenges of inclusion of omics studies into clinical studies. The first is the design of clinical studies. The second is the lack of approaches to minimize variability and increase reproducibility in the omics studies with unbiased statistical techniques to provide a clear link between drug exposure and drug response. Availability of the large scale datasets that span the entire immunoproteome [41] could improve the understanding of response to treatment in inflammation and inflammatory diseases.
3. Network biology The interaction and collective behavior of certain sets of biological molecules that perform a specific task, such as protein synthesis, form functional units, identified as biological networks [3]. Network biology provides a framework to explore molecular complexity of particular diseases, including inflammation and inflammatory diseases. In addition, molecular relationships among distinct treatment outcomes, such as response to treatment, can be evaluated [7]. A challenge of network biology is to model quantitatively the dynamic properties of biological networks. Quantitative changes (increase or decrease) of the elements in a biological network following ‘an intervention’, i.e., administration of a drug, are compared to a control state. The end result is a collection of several snap-shots of
the same biological network from different treatment groups. Snapshots are then summarized and quantified using pathway activity level method [39]. In this method, changes in biological networks (snap-shots) are quantified based on omics data using a single metric for entire study. The same method successfully analyzed changes in gene expression in relevant biological networks following exposure to a toxic agent [38]. In pathway activity level method, an integrated approach is used, where transcriptomics data and network biology are integrated to characterize either a biological state or treatment groups. Integrating omics-based data with network biology identifies not only potential biomarkers but also possible toxicity pathways arising from the same pharmacological effects. Nevertheless, more effective method is required to incorporate omics studies into mechanistic understanding of drug effect. An early example of such an effort was developed by Foteinou et al., where an endotoxin-induced human inflammation PK/PD model was established by incorporating transcriptional responses [12]. The developed model was then used to predict clinical outcomes such as heart rate [13]. Selecting a relevant species for prediction of a first-in-human (FIH) dose is of utmost importance for development of mAb. Severe adverse events observed following administration of an anti-CD28 superagonistic mAb have highlighted the importance of nonclinical safety studies including selecting relevant species [33,5]. Gene and protein expression levels of the target and comparison of epitope for binding to mAb and pharmacological activity between animal species and human are all important factors for determining a relevant species for safety studies [5]. Equally complicated and comprehensive process is the search for translational biomarkers. Network biology-based modeling can help to determine a relevant species and translational biomarkers. For example, cross-species comparison of biological networks enables establishing whether a potential mechanism of action between the animal species and human is similar or different. Cross-species comparisons of certain biological entities (such as protein sequence, genome sequence) have been developed by evolutionary biology and comparative genomics [10]. One striking example is the protein sequence comparison of the CYP2A family of cytochrome P450 in mouse, rat, and human liver [47]. The rat enzyme varied significantly from the human and mouse enzyme. This difference in the protein sequence was translated into hepatotoxic
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metabolization of a substrate in rat whereas the same substrate was harmless in human and mouse [27]. Similar comparative analyses (yeast, rat, mouse, human and others) were performed across biological networks [37]. However, cross-species comparison of biological networks is not trivial as multiple features of biological networks contribute to their function. One such feature is network structure, which includes the relations between components of network and regulators and the functional information of the network components, e.g., protein sequence and protein structure. Combining multiple features of biological networks improves their cross-species comparison [37] Current network biology-based models have a major limitation: they are generated based on information obtained from literature mining or databases [6]. Therefore, network biology-based models may suffer from incompleteness or missing interactions. To some extent, using multiple database sources addresses these issues [43]. As our understanding of interactions in living systems improves, the network biology-based models will provide more informative framework for drug development. 4. Future directions Overview of stages for development of mAbs and related mathematical modeling approaches are depicted in Fig. 1. Structural biology and PK/PD modeling are essential tools for development of mAbs. There are initiatives to integrate systems biology-based modeling and omics data into preclinical and clinical studies. Such modeling could accelerate and improve development of new mAbs. Systems biologybased modeling is not yet well integrated into development of mAbs. In contrast, omics data with network biology can already be utilized for characterization and prioritization of candidates and identification for biomarkers. Paramount of information on inflammation and inflammatory disease from systems biology perspective is available. However, the main challenge is to translate systems biology modeling into quantitative (semi-) mechanistic information to facilitate development of mAbs. Acknowledgment The author acknowledges Gorazd Drozina, MD, PhD for reviewing this commentary. References [1] D. Abernethy, R. Altman, et al., Quantitative and Systems Pharmacology in the Post-genomic Era, in: R. Ward (Ed.), New Approaches to Discovering Drugs and Understanding Therapeutic Mechanisms, National Institute of Health 2011. [2] V. Ahmadzadeh, S. Farajnia, et al., Antibody humanization methods for development of therapeutic applications, Monoclon. Antib. Immunodiagn. Immunother. 33 (2) (2014) 67–73. [3] R. Albert, Scale-free networks in cell biology, J. Cell Sci. 118 (Pt 21) (2005) 4947– 4957. [4] D.M. Ashcroft, A.L. Wan Po, et al., Clinical measures of disease severity and outcome in psoriasis: a critical appraisal of their quality, Br. J. Dermatol. 141 (2) (1999) 185–191. [5] H. Attarwala, TGN1412: from discovery to disaster, J. Young Pharm. 2 (3) (2010) 332–336. [6] G.D. Bader, M.P. Cary, et al., Pathguide: a pathway resource list, Nucleic Acids Res. 34 (Database issue) (2006) D504–D506. [7] A.L. Barabasi, N. Gulbahce, et al., Network medicine: a network-based approach to human disease, Nat. Rev. Genet. 12 (1) (2011) 56–68. [8] M. Benson, R. Breitling, Network theory to understand microarray studies of complex diseases, Curr. Mol. Med. 6 (6) (2006) 695–701. [9] G. Bindea, B. Mlecnik, et al., ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks, Bioinformatics 25 (8) (2009) 1091–1093. [10] S.L. Burgess-Herbert, S.Y. Euling, Use of comparative genomics approaches to characterize interspecies differences in response to environmental chemicals: challenges, opportunities, and research needs, Toxicol. Appl. Pharmacol. 271 (3) (2013) 372–385. [11] S.E. Calvano, W. Xiao, et al., A network-based analysis of systemic inflammation in humans, Nature 437 (7061) (2005) 1032–1037. [12] P.T. Foteinou, S.E. Calvano, et al., Modeling endotoxin-induced systemic inflammation using an indirect response approach, Math. Biosci. 217 (1) (2009) 27–42.
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