ATG-101 Is a Tetravalent PD-L1×4-1BB Bispecific Antibody That Stimulates Antitumor Immunity through PD-L1 Blockade and PD-L1-Directed 4-1BB Activation.

Immune checkpoint inhibitors (ICI) have transformed cancer treatment. However, only a minority of patients achieve a profound response. Many patients are innately resistant while others acquire resistance to ICIs. Furthermore, hepatotoxicity and suboptimal efficacy have hampered the clinical development of agonists of 4-1BB, a promising immune-stimulating target. To effectively target 4-1BB and treat diseases resistant to ICIs, we engineered ATG-101, a tetravalent "2+2″ PD-L1×4-1BB bispecific antibody. ATG-101 bound PD-L1 and 4-1BB concurrently, with a greater affinity for PD-L1, and potently activated 4-1BB+ T cells when cross-linked with PD-L1-positive cells. ATG-101 activated exhausted T cells upon PD-L1 binding, indicating a possible role in reversing T-cell dysfunction. ATG-101 displayed potent antitumor activity in numerous in vivo tumor models, including those resistant or refractory to ICIs. ATG-101 greatly increased the proliferation of CD8+ T cells, the infiltration of effector memory T cells, and the ratio of CD8+ T/regulatory T cells in the tumor microenvironment (TME), rendering an immunologically "cold" tumor "hot." Comprehensive characterization of the TME after ATG-101 treatment using single-cell RNA sequencing further revealed an altered immune landscape that reflected increased antitumor immunity. ATG-101 was well tolerated and did not induce hepatotoxicity in non-human primates. According to computational semimechanistic pharmacology modeling, 4-1BB/ATG-101/PD-L1 trimer formation and PD-L1 receptor occupancy were both maximized at around 2 mg/kg of ATG-101, providing guidance regarding the optimal biological dose for clinical trials. In summary, by localizing to PD-L1-rich microenvironments and activating 4-1BB+ immune cells in a PD-L1 cross-linking-dependent manner, ATG-101 safely inhibits growth of ICI resistant and refractory tumors. SIGNIFICANCE: The tetravalent PD-L1×4-1BB bispecific antibody ATG-101 activates 4-1BB+ T cells in a PD-L1 cross-linking-dependent manner, minimizing the hepatotoxicity of existing 4-1BB agonists and suppressing growth of ICI-resistant tumors. See related commentary by Ha et al., p. 1546.

Quantitative modeling of approved and emerging therapeutics for modifying TTR levels in patients with Transthyretin Amyloid Cardiomyopathy.

Transthyretin amyloidosis is a rare yet lethal disease caused by an increase in the destabilization of transthyretin tetramer into monomers, leading to amyloid fibril aggregates in tissues. Multiple therapeutics have been developed to limit or halt disease progression by altering tetramer kinetics. Small molecules, like Tafamidis and AG10, stabilize the tetrameric structure while genetic therapies, like Patisiran and NTLA-002, limit the production of TTR protein by silencing genetic expression. Both of these interventions slow the accumulation of fibrils by intervening at different points in the tetramer-monomer-fibril pathway. We developed a mathematical model to compare the pharmacological efficacies of these modalities by comparing each drug's ability to reduce the rate of tetramer to monomer formation, or "tetrameric flux." The model was trained on in vitro tetramer data as well as clinical measurements of tetramer concentration in humans. Overall, genetic silencers reduced tetrameric flux more than small molecule stabilizers. Properties that led to an improvement in small molecule stabilizer function and potential benefit of gene therapy - small molecule combination were explored. This study exemplifies how modeling can be used to compare modalities with differing mechanisms of action.

Towards a platform quantitative systems pharmacology (QSP) model for preclinical to clinical translation of antibody drug conjugates (ADCs).

A next generation multiscale quantitative systems pharmacology (QSP) model for antibody drug conjugates (ADCs) is presented, for preclinical to clinical translation of ADC efficacy. Two HER2 ADCs (trastuzumab-DM1 and trastuzumab-DXd) were used for model development, calibration, and validation. The model integrates drug specific experimental data including in vitro cellular disposition data, pharmacokinetic (PK) and tumor growth inhibition (TGI) data for T-DM1 and T-DXd, as well as system specific data such as properties of HER2, tumor growth rates, and volumes. The model incorporates mechanistic detail at the intracellular level, to account for different mechanisms of ADC processing and payload release. It describes the disposition of the ADC, antibody, and payload inside and outside of the tumor, including binding to off-tumor, on-target sinks. The resulting multiscale PK model predicts plasma and tumor concentrations of ADC and payload. Tumor payload concentrations predicted by the model were linked to a TGI model and used to describe responses following ADC administration to xenograft mice. The model was translated to humans and virtual clinical trial simulations were performed that successfully predicted progression free survival response for T-DM1 and T-DXd for the treatment of HER2+ metastatic breast cancer, including differential efficacy based upon HER2 expression status. In conclusion, the presented model is a step toward a platform QSP model and strategy for ADCs, integrating multiple types of data and knowledge to predict ADC efficacy. The model has potential application to facilitate ADC design, lead candidate selection, and clinical dosing schedule optimization.

A Quantitative Systems Pharmacology Model Describing the Cellular Kinetic-Pharmacodynamic Relationship for a Live Biotherapeutic Product to Support Microbiome Drug Development.

Live biotherapeutic products (LBPs) are human microbiome therapies showing promise in the clinic for a range of diseases and conditions. Describing the kinetics and behavior of LBPs poses a unique modeling challenge because, unlike traditional therapies, LBPs can expand, contract, and colonize the host digestive tract. Here, we present a novel cellular kinetic-pharmacodynamic quantitative systems pharmacology model of an LBP. The model describes bacterial growth and competition, vancomycin effects, binding and unbinding to the epithelial surface, and production and clearance of butyrate as a therapeutic metabolite. The model is calibrated and validated to published data from healthy volunteers. Using the model, we simulate the impact of treatment dose, frequency, and duration as well as vancomycin pretreatment on butyrate production. This model enables model-informed drug development and can be used for future microbiome therapies to inform decision making around antibiotic pretreatment, dose selection, loading dose, and dosing duration.

Two heads are better than one: current landscape of integrating QSP and machine learning : An ISoP QSP SIG white paper by the working group on the integration of quantitative systems pharmacology and machine learning.

Quantitative systems pharmacology (QSP) modeling is applied to address essential questions in drug development, such as the mechanism of action of a therapeutic agent and the progression of disease. Meanwhile, machine learning (ML) approaches also contribute to answering these questions via the analysis of multi-layer 'omics' data such as gene expression, proteomics, metabolomics, and high-throughput imaging. Furthermore, ML approaches can also be applied to aspects of QSP modeling. Both approaches are powerful tools and there is considerable interest in integrating QSP modeling and ML. So far, a few successful implementations have been carried out from which we have learned about how each approach can overcome unique limitations of the other. The QSP + ML working group of the International Society of Pharmacometrics QSP Special Interest Group was convened in September, 2019 to identify and begin realizing new opportunities in QSP and ML integration. The working group, which comprises 21 members representing 18 academic and industry organizations, has identified four categories of current research activity which will be described herein together with case studies of applications to drug development decision making. The working group also concluded that the integration of QSP and ML is still in its early stages of moving from evaluating available technical tools to building case studies. This paper reports on this fast-moving field and serves as a foundation for future codification of best practices.

Quantitative modeling predicts competitive advantages of a next generation anti-NKG2A monoclonal antibody over monalizumab for the treatment of cancer.

A semimechanistic pharmacokinetic (PK)/receptor occupancy (RO) model was constructed to differentiate a next generation anti-NKG2A monoclonal antibody (KSQ mAb) from monalizumab, an immune checkpoint inhibitor in multiple clinical trials for the treatment of solid tumors. A three-compartment model incorporating drug PK, biodistribution, and NKG2A receptor interactions was parameterized using monalizumab PK, in vitro affinity measurements for both monalizumab and KSQ mAb, and receptor burden estimates from the literature. Following calibration against monalizumab PK data in patients with rheumatoid arthritis, the model successfully predicted the published PK and RO observed in gynecological tumors and in patients with squamous cell carcinoma of the head and neck. Simulations predicted that the KSQ mAb requires a 10-fold lower dose than monalizumab to achieve a similar RO over a 3-week period following q3w intravenous (i.v.) infusion dosing. A global sensitivity analysis of the model indicated that the drug-target binding affinity greatly affects the tumor RO and that an optimal affinity is needed to balance RO with enhanced drug clearance due to target mediated drug disposition. The model predicted that the KSQ mAb can be dosed over a less frequent regimen or at lower dose levels than the current monalizumab clinical dosing regimen of 10 mg/kg q2w. Either dosing strategy represents a competitive advantage over the current therapy. The results of this study demonstrate a key role for mechanistic modeling in identifying optimal drug parameters to inform and accelerate progression of mAb to clinical trials.

Distant Non-Obvious Mutations Influence the Activity of a Hyperthermophilic Pyrococcusfuriosus Phosphoglucose Isomerase.

The cupin-type phosphoglucose isomerase (PfPGI) from the hyperthermophilic archaeon Pyrococcus furiosus catalyzes the reversible isomerization of glucose-6-phosphate to fructose-6-phosphate. We investigated PfPGI using protein-engineering bioinformatics tools to select functionally-important residues based on correlated mutation analyses. A pair of amino acids in the periphery of PfPGI was found to be the dominant co-evolving mutation. The position of these selected residues was found to be non-obvious to conventional protein engineering methods. We designed a small smart library of variants by substituting the co-evolved pair and screened their biochemical activity, which revealed their functional relevance. Four mutants were further selected from the library for purification, measurement of their specific activity, crystal structure determination, and metal cofactor coordination analysis. Though the mutant structures and metal cofactor coordination were strikingly similar, variations in their activity correlated with their fine-tuned dynamics and solvent access regulation. Alternative, small smart libraries for enzyme optimization are suggested by our approach, which is able to identify non-obvious yet beneficial mutations.

Modulating D-amino acid oxidase (DAAO) substrate specificity through facilitated solvent access.

D-amino acid oxidase (DAAO) degrades D-amino acids to produce α-ketoacids, hydrogen peroxide and ammonia. DAAO has often been investigated and engineered for industrial and clinical applications. We combined information from literature with a detailed analysis of the structure to engineer mammalian DAAOs. The structural analysis was complemented with molecular dynamics simulations to characterize solvent accessibility and product release mechanisms. We identified non-obvious residues located on the loops on the border between the active site and the secondary binding pocket essential for pig and human DAAO substrate specificity and activity. We engineered DAAOs by mutating such critical residues and characterised the biochemical activity of the resulting variants. The results highlight the importance of the selected residues in modulating substrate specificity, product egress and enzyme activity, suggesting further steps of DAAO re-engineering towards desired clinical and industrial applications.

CorNet: Assigning function to networks of co-evolving residues by automated literature mining.

CorNet is a web-based tool for the analysis of co-evolving residue positions in protein super-family sequence alignments. CorNet projects external information such as mutation data extracted from literature on interactively displayed groups of co-evolving residue positions to shed light on the functions associated with these groups and the residues in them. We used CorNet to analyse six enzyme super-families and found that groups of strongly co-evolving residues tend to consist of residues involved in a same function such as activity, specificity, co-factor binding, or enantioselectivity. This finding allows to assign a function to residues for which no data is available yet in the literature. A mutant library was designed to mutate residues observed in a group of co-evolving residues predicted to be involved in enantioselectivity, but for which no literature data is available yet. The resulting set of mutations indeed showed many instances of increased enantioselectivity.

StrandAdvantage test for early-line and advanced-stage treatment decisions in solid tumors.

Comprehensive genetic profiling of tumors using next-generation sequencing (NGS) is gaining acceptance for guiding treatment decisions in cancer care. We designed a cancer profiling test combining both deep sequencing and immunohistochemistry (IHC) of relevant cancer targets to aid therapy choices in both standard-of-care (SOC) and advanced-stage treatments for solid tumors. The SOC report is provided in a short turnaround time for four tumors, namely lung, breast, colon, and melanoma, followed by an investigational report. For other tumor types, an investigational report is provided. The NGS assay reports single-nucleotide variants (SNVs), copy number variations (CNVs), and translocations in 152 cancer-related genes. The tissue-specific IHC tests include routine and less common markers associated with drugs used in SOC settings. We describe the standardization, validation, and clinical utility of the StrandAdvantage test (SA test) using more than 250 solid tumor formalin-fixed paraffin-embedded (FFPE) samples and control cell line samples. The NGS test showed high reproducibility and accuracy of >99%. The test provided relevant clinical information for SOC treatment as well as more information related to investigational options and clinical trials for >95% of advanced-stage patients. In conclusion, the SA test comprising a robust and accurate NGS assay combined with clinically relevant IHC tests can detect somatic changes of clinical significance for strategic cancer management in all the stages.

Expression of tripartite motif-containing protein 28 in primary breast carcinoma predicts metastasis and is involved in the stemness, chemoresistance, and tumor growth.

The prediction of who develops metastasis has been the most difficult aspect in the management of breast cancer patients. The lymph node metastasis has been the most useful predictor of prognosis and patient management. However, a good proportion of patients with lymph node positivity remain disease free for 5 years or more, while about a third of those who were lymph node negative develop distant metastasis within the same period. This warrants a robust biomarker(s), preferably gene expression based. In order to elucidate gene-based biomarkers for prognosis of breast cancers, gene expression profiling of primary tumors and follow-up for over 5 years has been performed. The analysis revealed a network of genes centered around the tripartite motif-containing protein 28 as an important indicator of disease progression. Short hairpin RNA-mediated knockdown of tripartite motif-containing protein 28 in breast cancer cells revealed a decreased expression of epithelial-to-mesenchymal transition markers and increased expression of epithelial markers, decreased migration and invasion, and increased chemosensitivity to doxorubicin, 5-fluorouracil, and methotrexate. Furthermore, knockdown of tripartite motif-containing protein 28 resulted in the decrease of stemness as revealed by sphere formation assay as well as decreased expression of CD44 and Bmi1. Moreover, tripartite motif-containing protein 28 knockdown significantly reduced the tumor size and lung metastasis in orthotopic tumor xenograft assay in immunocompromised mice. The tumor size was further reduced when these mice were treated with doxorubicin. These data provide evidence for tripartite motif-containing protein 28 as a biomarker and a potential therapeutic target for breast cancer.

Identifying a Clinically Applicable Mutational Burden Threshold as a Potential Biomarker of Response to Immune Checkpoint Therapy in Solid Tumors.

PURPOSE: An association between mutational burden and response to immune checkpoint therapy has been documented in several cancer types. The potential for such a mutational burden threshold to predict response to immune checkpoint therapy was evaluated in several clinical datasets, where mutational burden was measured either by whole-exome sequencing (WXS) or using commercially available sequencing panels. METHODS: WXS and RNA-seq data of 33 solid cancer types from TCGA were analyzed to determine whether a robust immune checkpoint activating mutation (iCAM) burden threshold associated with evidence of immune checkpoint activation exists in these cancers that may serve as a biomarker for response to immune checkpoint blockade therapy. RESULTS: We find that a robust iCAM threshold, associated with signatures of immune checkpoint activation, exists in 8 of 33 solid cancers: melanoma, lung adenocarcinoma, colon adenocarcinoma, endometrial cancer, stomach adenocarcinoma, cervical cancer, ER+HER2- breast cancer, and bladder-urothelial cancer. Tumors with mutational burden higher than the threshold (iCAM+) also had clear histologic evidence of lymphocytic infiltration. In published datasets of melanoma, lung adenocarcinoma and colon cancer, patients with iCAM+ tumors had significantly better response to immune checkpoint therapy compared to those with iCAM- tumors. ROC analysis using TCGA predictions as gold standard showed that iCAM+ tumors are accurately identifiable using clinical sequencing assays, such as FoundationOne or StrandAdvantage. Using the FoundationOne derived threshold, analysis of 113 melanoma tumors, showed that iCAM+ patients have significantly better response to immune checkpoint therapy. iCAM+ and iCAM- tumors have distinct mutation patterns and different immune microenvironments. CONCLUSION: In 8 solid cancers, a mutational burden threshold exists that may predict response to immune checkpoint blockade. This threshold is identifiable using available clinical sequencing assays.

Next-generation sequencing-based method shows increased mutation detection sensitivity in an Indian retinoblastoma cohort.

PURPOSE: Retinoblastoma (Rb) is the most common primary intraocular cancer of childhood and one of the major causes of blindness in children. India has the highest number of patients with Rb in the world. Mutations in the RB1 gene are the primary cause of Rb, and heterogeneous mutations are distributed throughout the entire length of the gene. Therefore, genetic testing requires screening of the entire gene, which by conventional sequencing is time consuming and expensive. METHODS: In this study, we screened the RB1 gene in the DNA isolated from blood or saliva samples of 50 unrelated patients with Rb using the TruSight Cancer panel. Next-generation sequencing (NGS) was done on the Illumina MiSeq platform. Genetic variations were identified using the Strand NGS software and interpreted using the StrandOmics platform. RESULTS: We were able to detect germline pathogenic mutations in 66% (33/50) of the cases, 12 of which were novel. We were able to detect all types of mutations, including missense, nonsense, splice site, indel, and structural variants. When we considered bilateral Rb cases only, the mutation detection rate increased to 100% (22/22). In unilateral Rb cases, the mutation detection rate was 30% (6/20). CONCLUSIONS: Our study suggests that NGS-based approaches increase the sensitivity of mutation detection in the RB1 gene, making it fast and cost-effective compared to the conventional tests performed in a reflex-testing mode.

Detection of high frequency of mutations in a breast and/or ovarian cancer cohort: implications of embracing a multi-gene panel in molecular diagnosis in India.

Breast and/or ovarian cancer (BOC) are among the most frequently diagnosed forms of hereditary cancers and leading cause of death in India. This emphasizes on the need for a cost-effective method for early detection of these cancers. We sequenced 141 unrelated patients and families with BOC using the TruSight Cancer panel, which includes 13 genes strongly associated with risk of inherited BOC. Multi-gene sequencing was done on the Illumina MiSeq platform. Genetic variations were identified using the Strand NGS software and interpreted using the StrandOmics platform. We were able to detect pathogenic mutations in 51 (36.2%) cases, out of which 19 were novel mutations. When we considered familial breast cancer cases only, the detection rate increased to 52%. When cases were stratified based on age of diagnosis into three categories, ⩽40 years, 40-50 years and >50 years, the detection rates were higher in the first two categories (44.4% and 53.4%, respectively) as compared with the third category, in which it was 26.9%. Our study suggests that next-generation sequencing-based multi-gene panels increase the sensitivity of mutation detection and help in identifying patients with a high risk of developing cancer as compared with sequential tests of individual genes.

Building shared experience to advance practical application of pathway-based toxicology: liver toxicity mode-of-action.

A workshop sponsored by the Human Toxicology Project Consortium (HTPC), "Building Shared Experience to Advance Practical Application of Pathway-Based Toxicology: Liver Toxicity Mode-of-Action" brought together experts from a wide range of perspectives to inform the process of pathway development and to advance two prototype pathways initially developed by the European Commission Joint Research Center (JRC): liver-specific fibrosis and steatosis. The first half of the workshop focused on the theory and practice of pathway development; the second on liver disease and the two prototype pathways. Participants agreed pathway development is extremely useful for organizing information and found that focusing the theoretical discussion on a specific AOP is extremely helpful. In addition, it is important to include several perspectives during pathway development, including information specialists, pathologists, human health and environmental risk assessors, and chemical and product manufacturers, to ensure the biology is well captured and end use is considered.

A systems biology based integrative framework to enhance the predictivity of in vitro methods for drug-induced liver injury.

BACKGROUND: Liver injury is the most common cause of postmarketing withdrawal of drugs. Traditional animal toxicity testing methods have proved to be imperfect tools for predicting toxicity observed in the clinic. OBJECTIVE: Predictive methods that integrate data and insights from several in vitro methods to provide a deeper understanding of the impact of a drug on the liver are the need of the hour. METHOD: A systems approach based on mathematical modelling using the kinetics of biochemical pathways involved in liver homeostasis coupled with in vitro measurements to quantify drug-induced perturbations is described here. CONCLUSIONS: Integrating in silico and in vitro methods provides a powerful platform that allows reasonably accurate and mechanistic-level prediction of drug-induced liver injury. The method demonstrates that several physiological situations can be accurately modelled as can the effect of perturbations induced by drugs. It can also be used along with high-throughput 'omic' data to generate testable hypotheses leading to informed decision-making.

truPK -- human pharmacokinetic models for quantitative ADME prediction.

The use of in silico prediction of absorption, distribution, metabolism and excretion (ADME) properties is gaining acceptance as a useful assessment tool for early identification of likely drug candidate failures. However, it has been difficult to locate reliable models for the prediction of human pharmacokinetics (PK) in silico Currently available methods for estimating ADME and toxicity properties, such as in vitro and animal models, are not very predictive of what is observed in the clinic. Existing in silico ADME prediction tools concentrate on physicochemical properties, such as solubility, log P, rule-of-five compliance, Caco-2 permeability, blood-brain barrier and so on, or only classify drug-like candidates as 'poor', 'medium' or 'good' for a PK parameter, without ascribing values. Although physiology-based pharmacokinetic -models can predict ADME properties, they rely on using various measured properties as input for better accuracy. Strand Genomics has developed a tool, truPK, that predicts the properties of a molecule (bioavailability, protein binding, volume of distribution, elimination half-life and absorption rate) that affect its dose and dose frequency in humans. truPK's five models built using sophisticated machine methods have predicted with > 75% accuracies in external validation sets.