Technological developments for small-scale downstream processing of cell therapies.

Despite considerable regulatory and clinical hurdles, the development and use of cell-based therapies are gaining momentum. As more of these therapies move toward commercial approval and larger-scale distribution, associated manufacturing and processing technologies are being advanced. Modern technologies directed at downstream processing seek to distribute such therapies from the manufacturing site to the patient more efficiently and reliably. Novel small-scale downstream solutions boost the transformation of cell therapies from abstraction to reality.

Secretory protein profiling reveals TNF-α inactivation by selective and promiscuous Sec61 modulators.

Cotransins are cyclic heptadepsipeptides that bind the Sec61 translocon to inhibit cotranslational translocation of a subset of secreted and type I transmembrane proteins. The few known cotransin-sensitive substrates are all targeted to the translocon by a cleavable signal sequence, previously shown to be a critical determinant of cotransin sensitivity. By profiling two cotransin variants against a panel of secreted and transmembrane proteins, we demonstrate that cotransin side-chain differences profoundly affect substrate selectivity. Among the most sensitive substrates we identified is the proinflammatory cytokine tumor necrosis factor alpha (TNF-α). Like all type II transmembrane proteins, TNF-α is targeted to the translocon by its membrane-spanning domain, indicating that a cleavable signal sequence is not strictly required for cotransin sensitivity. Our results thus reveal an unanticipated breadth of translocon substrates whose expression is inhibited by Sec61 modulators.

Discovery of dual inhibitors of the immune cell PI3Ks p110delta and p110gamma: a prototype for new anti-inflammatory drugs.

PI3Kdelta and PI3Kgamma regulate immune cell signaling, while the related PI3Kalpha and PI3Kbeta regulate cell survival and metabolism. Selective inhibitors of PI3Kdelta/gamma represent a potential class of anti-inflammatory agents lacking the antiproliferative effects associated with PI3Kalpha/beta inhibition. Here we report the discovery of PI3Kdelta/gamma inhibitors that display up to 1000-fold selectivity over PI3Kalpha/beta and evaluate these compounds in a high-content inflammation assay using mixtures of primary human cells. We find selective inhibition of only PI3Kdelta is weakly anti-inflammatory, but PI3Kdelta/gamma inhibitors show superior inflammatory marker suppression through suppression of lipopolysaccharide-induced TNFalpha production and T cell activation. Moreover, PI3Kdelta/gamma inhibition yields an anti-inflammatory signature distinct from pan-PI3K inhibition and known anti-inflammatory drugs, yet bears striking similarities to glucocorticoid receptor agonists. These results highlight the potential of selectively designing drugs that target kinases with shared biological function.

Chemical target and pathway toxicity mechanisms defined in primary human cell systems.

INTRODUCTION: The ability to predict the health effects resulting from drug or chemical exposure has been challenging due to the complexity of human biology. Approaches that detect and discriminate a broad range of mechanisms in testing formats that are predictive and yet cost-effective are needed. METHODS: Here, we evaluated the performance of BioMAP systems, primary human cell-based disease models, as a platform for characterization of chemical toxicity mechanisms. For this we tested a set of compounds with known or well-studied mechanisms in a panel of 8 BioMAP assays relevant to human respiratory, skin, immune and vascular exposure sites. RESULTS: We evaluated the ability to detect and distinguish compounds based on mechanisms of action, comparing the BioMAP activity profiles generated in a reduced sample number format to reference database profiles derived from multiple experiments. We also studied the role of BioMAP assay panel size and concentration effects, both of which were found to contribute to the ability to discriminate chemicals and mechanisms. DISCUSSION: Compounds with diverse mechanisms, including modulators of the NFkappaB pathway, microtubule function and mitochondrial activity, could be discriminated and classified into target and pathway mechanisms in both assay formats. Certain inhibitors of mitochondrial function, such as rotenone and sodium azide, but not others, were classified with inducers of endoplasmic reticulum stress, providing insight into the toxicity mechanisms of these agents. This method may have utility in classifying novel agents with unknown modes of action according to their effects on toxicity pathways.

Key role of local regulation in chemosensing revealed by a new molecular interaction-based modeling method.

The signaling network underlying eukaryotic chemosensing is a complex combination of receptor-mediated transmembrane signals, lipid modifications, protein translocations, and differential activation/deactivation of membrane-bound and cytosolic components. As such, it provides particularly interesting challenges for a combined computational and experimental analysis. We developed a novel detailed molecular signaling model that, when used to simulate the response to the attractant cyclic adenosine monophosphate (cAMP), made nontrivial predictions about Dictyostelium chemosensing. These predictions, including the unexpected existence of spatially asymmetrical, multiphasic, cyclic adenosine monophosphate-induced PTEN translocation and phosphatidylinositol-(3,4,5)P3 generation, were experimentally verified by quantitative single-cell microscopy leading us to propose significant modifications to the current standard model for chemoattractant-induced biochemical polarization in this organism. Key to this successful modeling effort was the use of "Simmune," a new software package that supports the facile development and testing of detailed computational representations of cellular behavior. An intuitive interface allows user definition of complex signaling networks based on the definition of specific molecular binding site interactions and the subcellular localization of molecules. It automatically translates such inputs into spatially resolved simulations and dynamic graphical representations of the resulting signaling network that can be explored in a manner that closely parallels wet lab experimental procedures. These features of Simmune were critical to the model development and analysis presented here and are likely to be useful in the computational investigation of many aspects of cell biology.

Characterization of compound mechanisms and secondary activities by BioMAP analysis.

INTRODUCTION: Unexpected drug activities account for many of the failures of new chemical entities in clinical trials. These activities can be target-dependent, resulting from feedback mechanisms downstream of the primary target, or they can occur as a result of unanticipated secondary target(s). Methods that would provide rapid and efficient characterization of compounds with respect to a broad range of biological pathways and mechanisms relevant to human disease have the potential to improve preclinical and clinical success rates. METHODS: BioMAP assays containing primary human cells (endothelial cells and co-cultures with peripheral blood leukocytes) were stimulated in complex formats (specific combinations of inflammatory mediators) for 24 h in the presence or absence of test agents (drugs, experimental compounds, etc.). The levels of selected protein readouts (adhesion receptors, cytokines, enzymes, etc.) were measured and activity profiles (normalized data sets comprising BioMAP profiles) were generated for each test agent. The resulting profiles were compared by statistical methods to identify similarities and mechanistic insights. RESULTS: Compounds with known mechanisms including inhibitors of histamine H1 receptor, angiotensin converting enzyme, IkappaB kinase-2, beta2 adrenergic receptor and others were shown to generate reproducible and distinguishable BioMAP activity profiles. Similarities were observed between compounds targeting components within the same signal transduction pathway (e.g. NFkappaB), and also between compounds that share secondary targets (e.g. ibuprofen and FMOC-L-leucine, a PPARgamma agonist). DISCUSSION: Complex primary cell-based assays can be applied for detecting and distinguishing unexpected activities that may be of relevance to drug action in vivo. The ability to rapidly test compounds prior to animal or clinical studies may reduce the number of compounds that unexpectedly fail in preclinical or clinical studies.

A substrate-specific inhibitor of protein translocation into the endoplasmic reticulum.

The segregation of secretory and membrane proteins to the mammalian endoplasmic reticulum is mediated by remarkably diverse signal sequences that have little or no homology with each other. Despite such sequence diversity, these signals are all recognized and interpreted by a highly conserved protein-conducting channel composed of the Sec61 complex. Signal recognition by Sec61 is essential for productive insertion of the nascent polypeptide into the translocation site, channel gating and initiation of transport. Although subtle differences in these steps can be detected between different substrates, it is not known whether they can be exploited to modulate protein translocation selectively. Here we describe cotransin, a small molecule that inhibits protein translocation into the endoplasmic reticulum. Cotransin acts in a signal-sequence-discriminatory manner to prevent the stable insertion of select nascent chains into the Sec61 translocation channel. Thus, the range of substrates accommodated by the channel can be specifically and reversibly modulated by a cell-permeable small molecule that alters the interaction between signal sequences and the Sec61 complex.

Approaches to the analysis of cell signaling networks and their application in drug discovery.

The ability to predict the safety and efficacy of novel drugs prior to clinical testing is a key goal in pharmaceutical drug discovery. Gaining a mechanistic understanding of the complex cell signaling networks (CSNs) underlying disease processes promises to help reduce the number of clinical failures by identifying points of intervention as well as redundancies and feedback mechanisms that contribute to toxicities, lack of efficacy and unexpected biological activities. Experimental and computational approaches to analyzing and modeling CSNs are currently being validated using simple organisms and cell lines. In vitro cell systems of sufficient complexity to resemble human disease physiology, but which are also amenable to chemical and genetic perturbations on a large scale, are now required for deciphering the signaling networks operating in human disease. In this review, experimental and computational methods for modeling complex CSNs and the applications of these approaches to pharmaceutical drug discovery are discussed.

Systems biology in drug discovery.

The hope of the rapid translation of 'genes to drugs' has foundered on the reality that disease biology is complex, and that drug development must be driven by insights into biological responses. Systems biology aims to describe and to understand the operation of complex biological systems and ultimately to develop predictive models of human disease. Although meaningful molecular level models of human cell and tissue function are a distant goal, systems biology efforts are already influencing drug discovery. Large-scale gene, protein and metabolite measurements ('omics') dramatically accelerate hypothesis generation and testing in disease models. Computer simulations integrating knowledge of organ and system-level responses help prioritize targets and design clinical trials. Automation of complex primary human cell-based assay systems designed to capture emergent properties can now integrate a broad range of disease-relevant human biology into the drug discovery process, informing target and compound validation, lead optimization, and clinical indication selection. These systems biology approaches promise to improve decision making in pharmaceutical development.