Filter inference: a scalable nonlinear mixed effects inference approach for snapshot time series data

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling

A Novel Approach for Quantifying the Pharmacological Activity of T-Cell Engagers Utilizing In Vitro Time Course Experiments and Streamlined Data Analysis

CD3-bispecifc antibodies are a new class of immunotherapeutic drugs against cancer. The pharmacological activity of CD3-bispecifcs is typically assessed through in vitro assays of cancer cell lines co-cultured with human peripheral blood mononuclear cells (PBMCs). Assay results depend on experimental conditions such as incubation time and the efector-to-target cell ratio, which can hinder robust quantifcation of pharmacological activity. In order to overcome these limitations, we developed a new, holistic approach for quantifcation of the in vitro dose–response relationship. Our experimental design integrates a time-independent analysis of the dose–response across diferent time points as an alternative to the static, “snap-shot” analysis based on a single time point commonly used in dose–response assays. We show that the potency values derived from staticin vitro experiments depend on the incubation time, which leads to inconsistent results across multiple assays and compounds. We compared the potency values from the time-independent analysis with a model-based approach. We fnd comparably accurate potency estimates from the model-based and time-independent analyses and that the timeindependent analysis provides a robust quantifcation of pharmacological activity. This approach may allow for an improved head-to-head comparison of diferent compounds and test systems and may prove useful for supporting frst-in-human dose selection

Pharmacokinetics and pharmacodynamics of t-cell bispecifics in the tumour interstitial fluid

The goal of this study is to investigate the pharmacokinetics in plasma and tumour interstitial fluid of two T-cell bispecifics (TCBs) with different binding affinities to the tumour target and to assess the subsequent cytokine release in a tumour-bearing humanised mouse model. Pharmacokinetics (PK) as well as cytokine data were collected in humanised mice after iv injection of cibisatamab and CEACAM5-TCB which are binding with different binding affinities to the tumour antigen carcinoembryonic antigen (CEA). The PK data were modelled and coupled to a previously published physiologically based PK model. Corresponding cytokine release profiles were compared to in vitro data. The PK model provided a good fit to the data and precise estimation of key PK parameters. High tumour interstitial concentrations were observed for both TCBs, influenced by their respective target binding affinities. In conclusion, we developed a tailored experimental method to measure PK and cytokine release in plasma and at the site of drug action, namely in the tumour. Integrating those data into a mathematical model enabled to investigate the impact of target affinity on tumour accumulation and can have implications for the PKPD assessment of the therapeutic antibodies.publishedVersio

Biochemical studies on the interaction of the aerobic sn-glycerol-3-phosphate dehydrogenase from Escherichia coli with the cytoplasmic membrane

Die aerobe sn-Glycerin-3-Phosphat-Dehydrogenase (GlpD) aus Escherichia coli ist ein lösliches Enzym, das in vivo durch die Bindung an die cytoplasmatische Membran aktiviert wird. Diese Arbeit beschreibt den zugrunde liegenden molekularen Mechanismus der GlpD-Membran-Bindung.Durch Kompetitionsstudien wurde gezeigt, dass die Bindung von GlpD an eukaryontisches Calmodulin die GlpD-Membran Bindung widerspiegelt. Mittels C-terminal verkürzter GlpD-Fragmente und mittels in-vitro-Mutagenese wurde die CaM-Bindedomäne von GlpD auf den Bereich von Aminosäuren 355-370, einer basisch amphiphilen alpha-Helix, eingegrenzt. Punktmutationen, die elektropositive Aminosäuren gegen elektronegative im hydrophilen Teil dieser Helix austauschen, beeinträchtigten die CaM-Bindung.Direkte GlpD-Lipid Interaktion wurde auf zwei unterschiedlichen Wegen demonstriert. Mithilfe der Monolayer-Technik wurde die penetrative Kraft von GlpD gegenüber unterschiedlich zusammengesetzten Phospholipid-Monolayern gemessen. Unter Verwendung von Phospholipid-Bilayern wurde in einem zweiten Ansatz nachgewiesen, dass GlpD an die Oberfläche von Liposomen bindet. Die Membraninsertion von GlpD war abhängig von der Lipidzusammensetzung: In Experimenten mit gemischten Phospholipiden wurde eine positive Korrelation zwischen dem Gehalt an negativ geladenen Phospholipiden und der induzierten Druckänderung beobachtet. Die Punktmutanten, die nicht mehr an Calmodulin banden, konnten bei physiologisch relevanten Drücken nicht mehr in Lipid-Monolayer inserieren. Experimente mit Lipid-Bilayern bestätigten, dass eine direkte GlpD-Lipid-Bindung besteht

Aerobic sn-glycerol-3-phosphate dehydrogenase from Escherichia coli binds to the cytoplasmic membrane through an amphipathic alpha-helix.

sn-Glycerol-3-phosphate dehydrogenase (GlpD) from Escherichia coli is a peripheral membrane enzyme involved in respiratory electron transfer. For it to display its enzymic activity, binding to the inner membrane is required. The way the enzyme interacts with the membrane and how this controls activity has not been elucidated. In the present study we provide evidence for direct protein-lipid interaction. Using the monolayer technique, we observed insertion of GlpD into lipid monolayers with a clear preference for anionic phospholipids. GlpD variants with point mutations in their predicted amphipathic helices showed a decreased ability to penetrate anionic phospholipid monolayers. From these data we propose that membrane binding of GlpD occurs by insertion of an amphipathic helix into the acyl-chain region of lipids mediated by negatively charged phospholipids

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data.

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference uses measurements of simulated individuals to define an approximate likelihood for the model parameters, avoiding the computational limitations of traditional NLME inference approaches and making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling

Pharmacokinetics and pharmacodynamics of t-cell bispecifics in the tumour interstitial fluid

The goal of this study is to investigate the pharmacokinetics in plasma and tumour interstitial fluid of two T-cell bispecifics (TCBs) with different binding affinities to the tumour target and to assess the subsequent cytokine release in a tumour-bearing humanised mouse model. Pharmacokinetics (PK) as well as cytokine data were collected in humanised mice after iv injection of cibisatamab and CEACAM5-TCB which are binding with different binding affinities to the tumour antigen carcinoembryonic antigen (CEA). The PK data were modelled and coupled to a previously published physiologically based PK model. Corresponding cytokine release profiles were compared to in vitro data. The PK model provided a good fit to the data and precise estimation of key PK parameters. High tumour interstitial concentrations were observed for both TCBs, influenced by their respective target binding affinities. In conclusion, we developed a tailored experimental method to measure PK and cytokine release in plasma and at the site of drug action, namely in the tumour. Integrating those data into a mathematical model enabled to investigate the impact of target affinity on tumour accumulation and can have implications for the PKPD assessment of the therapeutic antibodies

Phase I clinical study of RG7356, an anti-CD44 humanized antibody, in patients with acute myeloid leukemia

RG7356, a recombinant anti-CD44 immunoglobulin G1 humanized monoclonal antibody, inhibits cell adhesion and has been associated with macrophage activation in preclinical models. We report results of a phase I dose-escalation study of RG7356 in relapsed/refractory acute myeloid leukemia (AML). Eligible patients with refractory AML, relapsed AML after induction chemotherapy, or previously untreated AML not eligible for intensive chemotherapy were enrolled and received intravenous RG7356 at dosages ≤ 2400 mg every other week or ≤ 1200 mg weekly or twice weekly; dose escalation started at 300 mg. Forty-four patients (median age, 69 years) were enrolled. One dose-limiting toxicity occurred (grade 3 hemolysis exacerbation) after one 1200 mg dose (twice-weekly cohort). The majority of adverse events were mild/moderate. Infusion-related reactions occurred in 64% of patients mainly during cycle 1. Two patients experienced grade 3 drug-induced aseptic meningitis. Pharmacokinetics increased supraproportionally, suggesting a target-mediated drug disposition (TMDD) at ≥ 1200 mg. Two patients achieved complete response with incomplete platelet recovery or partial response, respectively. One patient had stable disease with hematologic improvement. RG7356 was generally safe and well tolerated. Maximum tolerated dose was not reached, but saturation of TMDD was achieved. The recommended dose for future AML evaluations is 2400 mg every other week

First-in-human phase I clinical trial of RG7356, an anti-CD44 humanized antibody, in patients with advanced, CD44-expressing solid tumors

Transmembrane glycoprotein CD44 is overexpressed in various malignancies. Interactions between CD44 and hyaluronic acid are associated with poor prognosis, making CD44 an attractive therapeutic target. We report results from a first-in-human phase I trial of RG7356, a recombinant anti-CD44 immunoglobulin G1 humanized monoclonal antibody, in patients with advanced CD44-expressing solid malignancies. Sixty-five heavily pretreated patients not amenable to standard therapy were enrolled and received RG7356 intravenously biweekly (q2w) or weekly (qw) in escalating doses from 100 mg to 2,250 mg. RG7356 was well tolerated. Most frequent adverse events were fever, headache and fatigue. Dose-limiting toxicities included headache (1,500 mg q2w and 1,350 mg qw) and febrile neutropenia (2,250 mg q2w). The maximum tolerated dose with q2w dosing was 1,500 mg, but was not defined for qw dosing due to early study termination. Clinical efficacy was modest; 13/61 patients (21%) experienced disease stabilization lasting a median of 12 (range, 6–35) weeks. No apparent dose- or dose schedule-dependent changes in biological activity were reported from blood or tissue analyses. Tumor-targeting by positron emission tomography (PET) using (89)Zr-labeled RG7356 was observed for doses ≥200 mg (q2w) warranting further investigation of this agent in combination regimens

Patient-derived micro-organospheres enable clinical precision oncology

Patient-derived xenografts (PDXs) and patient-derived organoids (PDOs) have been shown to model clinical response to cancer therapy. However, it remains challenging to use these models to guide timely clinical decisions for cancer patients. Here, we used droplet emulsion microfluidics with temperature control and dead-volume minimization to rapidly generate thousands of micro-organospheres (MOSs) from low-volume patient tissues, which serve as an ideal patient-derived model for clinical precision oncology. A clinical study of recently diagnosed metastatic colorectal cancer (CRC) patients using an MOS-based precision oncology pipeline reliably assessed tumor drug response within 14 days, a timeline suitable for guiding treatment decisions in the clinic. Furthermore, MOSs capture original stromal cells and allow T cell penetration, providing a clinical assay for testing immuno-oncology (IO) therapies such as PD-1 blockade, bispecific antibodies, and T cell therapies on patient tumors