Microglia-PET imaging as a surrogate marker for post-stroke neuroinflammation in preclinical mouse models and clinical cases: quantitative PET data analysis using biokinetic modeling and machine learning including information from multiparametric MRI scans

Background Ischemic stroke is the second leading cause of death and the third main cause of long-term disability worldwide, which explains the need for novel therapies to improve neurological recovery. Microglia, brain resident immune cells, are a suitable target for such a therapy. These cells express 18 kDa translocator protein (TSPO) when activated, which enables neuroinflammation monitoring using positron emission tomography (PET) with TSPO tracers, such as [18F]GE-180. However, the signal in PET images originates not only from specific binding of the tracer to the receptor of interest; it is contaminated by non-specific binding and free tracer in both tissue and blood. Gold-standard quantification of [18F]GE-180 specific binding is currently performed using pharmacokinetic modeling, which requires a longer scanning time and continuous arterial blood sampling. This is not only burdensome for the hospital staff, but also associated with additional risks and discomfort for the patient. Aim The aim of this work was to establish a simplified [18F]GE-180 PET scanning protocol for a mouse ischemic stroke model and translate it into human PET by integrating additional potentially relevant information using machine learning (ML) and taking a well-established pharmacokinetic modeling method as the ground truth. Materials and Methods Mouse study: Six mice after photothrombotic stroke (PT) and six sham mice were included in the study and scanned using a dedicated small-animal PET/MR scanner. For a half of the mice, we acquired four serial 0–90 min post injection (p.i.) scans per mouse (analysis cohort) and calculated quantitative TSPO binding estimates (distribution volume ratio, DVR) as well as semi-quantitative estimates (standardized uptake volume ratio, SUVR) for five late 10 min time frames. We compared how well the obtained SUVRs approximated DVR by means of linear fitting and Pearson correlation coefficient. The other half of the mice received 60-90 min p.i. [18F]GE-180 PET and was used as a validation cohort. Human study: 18 subjects after acute ischemic stroke received 0-90 min p.i. [18F]GE-180 PET along with a number of MRI sequences. Five manual venous blood samples were drawn during the PET scan and their activity concentration was measured. Based on dynamic PET data, a quantitative TSPO binding estimate was calculated voxelwise. We trained an ML algorithm using these estimates as the ground truth and three late 10 min PET frames, the ASL image, voxel co-ordinates, the lesion mask, and the five plasma activity concentrations as input features. Using Shapley Additive Explanations, we determined that the three late PET frames and the plasma activity concentrations had the highest impact on the model’s performance. We then tested a simplified quantification approach consisting of dividing a late PET frame by a plasma activity concentration. All the combinations of frames/samples were compared by means of concordance correlation coefficient and Bland-Altman plots. Results The mouse study showed that the 60–70, 70–80, and 80–90 min p.i. frames produce the closest approximation for 90 min scan-based DVR in both sham and PT mice. The human study demonstrated on an individual voxel basis an additional value of the late plasma activity concentration in approximating the quantitative 90 min scan-based TSPO estimate. The 70-80 min p.i. frame divided by the 30 min p.i. plasma sample produced the closest semi-quantitative estimate in the ischemic lesion. Conclusion Reliable simplified TSPO quantification in patients after acute ischemic stroke is achievable by using a short late PET frame divided by a late plasma activity concentration and can thus replace full quantification based on a 90 min dynamic scan. The ML-based procedure of estimating feature importance used in this work can be applied for other conditions and other tracers in the future.Hintergrund Der ischämische Schlaganfall ist weltweit die zweithäufigste Todesursache und die dritthäufigste Ursache für Langzeitbehinderungen, was den Bedarf an neuartigen Therapien zur Verbesserung der neurologischen Erholung erklärt. Mikroglia, Immunzellen im Gehirn, sind ein geeignetes Ziel für eine solche Therapie. Diese Zellen exprimieren das 18 kDa-Translokatorprotein (TSPO), wenn sie aktiviert sind, was die Messung von Neuroinflammation mittels Positronen-Emissions-Tomographie (PET) mit TSPO-Tracern wie [18F]GE-180 ermöglicht. Das Signal in den PET-Bildern stammt jedoch nicht nur von der spezifischen Bindung des Tracers an den betreffenden Rezeptor, sondern wird auch durch unspezifische Bindungen und freien Tracer im Gewebe und im Blut kontaminiert. Die Goldstandard-Quantifizierung der spezifischen Bindung von [18F]GE-180 wird derzeit mit Hilfe pharmakokinetischer Modelle durchgeführt, was eine längere Messzeit und eine kontinuierliche arterielle Blutentnahme erfordert. Dies ist nicht nur für das Krankenhauspersonal belastend, sondern auch mit zusätzlichen Risiken und Unannehmlichkeiten für die Patienten verbunden. Zielsetzung Ziel dieser Arbeit war es, ein vereinfachtes [18F]GE-180-PET-Scanprotokoll für ein ischämisches Schlaganfallmodell bei Mäusen zu erstellen und es auf die PET-Untersuchung bei Menschen zu übertragen, indem zusätzliche potenziell relevante Informationen mit Hilfe von maschinellem Lernen (ML) integriert werden und eine wohl etablierte pharmakokinetische Modellierungsmethode als Grundwahrheit verwendet wird. Material und Methoden Mausstudie: Sechs Mäuse nach photothrombotischem Schlaganfall (PT) und sechs Mäuse nach identischer Versuchsdurchführung, jedoch ohne Schlaganfall (sham), wurden in die Studie aufgenommen und mit einem dedizierten Kleintier-PET/MR-Scanner untersucht. Für die Hälfte der Mäuse wurden vier serielle Messungen 0-90 Minuten nach der Injektion (p.i.) pro Maus (Analysekohorte) durchgeführt und die TSPO_Bindung quantitativ geschätzt (Distribution Volume Ratio, DVR). Zusätzlich wurden semi-quantitative Schätzungen (Standardized Uptake Volume Ratio, SUVR) für fünf späte 10 min Zeitfenster berechnet. Wir verglichen die Eignung der SUVRs als Näherung für die DVR mittels linearer Anpassung und Pearson-Korrelationskoeffizient. Die andere Hälfte der Mäuse erhielt 60-90 min p.i. [18F]GE-180-PET und wurde als Validierungskohorte verwendet. Humanstudie: 18 Probanden erhielten nach einem akuten ischämischen Schlaganfall 0-90 min p.i. [18F]GE-180-PET zusammen mit einer Reihe von MRT-Sequenzen. Fünf manuelle venöse Blutproben wurden während des PET-Scans entnommen und ihre Aktivitätskonzentration gemessen. Auf der Grundlage der dynamischen PET-Daten wurde eine quantitative Schätzung der TSPO-Bindung voxelweise berechnet. Wir trainierten einen ML-Algorithmus, der diese Schätzungen als Grundwahrheit und drei späte 10 min PET-Bilder, das ASL-Bild, Voxelkoordinaten, die Läsionsmaske und die fünf Plasmaaktivitätskonzentrationen als Eingangsmerkmale verwendete. Unter Verwendung von Shapley Additive Explanations stellten wir fest, dass die drei späten PET-Bilder und die Plasmaaktivitätskonzentrationen den größten Einfluss auf die Qualität des Modells hatten. Anschließend testeten wir eine vereinfachte Quantifizierungsmethode, die darin bestand, ein spätes PET-Bild durch eine Plasmaaktivitätskonzentration zu dividieren. Alle Kombinationen von Bildern/Proben wurden anhand von Konkordanz-Korrelationskoeffizienten und Bland-Altman-Diagrammen verglichen. Ergebnisse Die Mausstudie zeigte, dass die 60-70, 70-80 und 80-90 min p.i. Zeitfenster die beste Näherung an die 90 min Scan basierte DVR sowohl bei den Sham- als auch bei den PT-Mäusen produzieren. Die Humanstudie zeigte auf der Basis individueller Voxel einen zusätzlichen Wert der späten Plasmaaktivitätskonzentration für die Näherung an die quantitative 90-min Scan-basierten TSPO-Schätzung. Die Division der Werte im 70-80 min p.i. Zeitfenster mit dem Messwert der 30 min p.i. Plasmaprobe ergab die genaueste semi-quantitative Schätzung in der ischämischen Läsion. Schlussfolgerung Eine zuverlässige vereinfachte TSPO-Quantifizierung bei Patienten nach einem akuten ischämischen Schlaganfall ist durch die Verwendung eines kurzen späten PET-Zeitfensters geteilt durch eine späte Plasmaaktivitätskonzentration möglich und kann somit eine vollständige Quantifizierung auf der Grundlage eines 90 min dynamischen Scans ersetzen. Das in dieser Arbeit verwendete ML-basierte Verfahren zur Schätzung der Relevanz verschiedener Merkmale kann in Zukunft auch für andere Erkrankungen und Tracer angewendet werden

Pathway Activity Analysis (PAA) as a new class of mechanistic biomarker to predict drug responses in drug repositioning for cancer patients

[EN] In recent years, progress in new technologies has resulted in the capacity to generate massive amounts of data, this is known as the "Omics Age". The challenge now is data integration and analysis. Thus, Systems Biology emerges as a solution; where, previously, genetic studies estimated the impact of a single gene, now all gene data can be integrated. This allows for more precise conclusions since diseases and drug responses are caused by different combinations of genetic perturbations. Furthermore, it allows for simulations that would otherwise be prohibitively costly in terms of time and resources. In this context, here is presented a new method for the integration of available data for each element of a signalling pathway in the end result of said pathway, the phenotype. This system acts as a mechanistic biomarker, since the difference in activation level present in a pathway, when comparing samples, serves to expose more information about the mechanisms which act in a different manner. A much more informative method than descriptive biomarkers. Additionally, this method allows simulations. When inputting information about a drug’s effects, the activity level of the pathway can be modified and an estimation of the desirability of the effects can be made. Cancer patients frequently respond in an undesirable manner to therapy, a great problem in oncology that is thought to be due to a lack of predictive biomarkers. The activity of pathways in cancerous cells can be used as mechanistic biomarkers. This project intends to exploit this new tool to reposition drugs for cancer patients.[ES] En los últimos años, los avances en nuevas tecnologías han permitido generar enormes cantidades de datos, la conocida “Era de las Ómicas”. El reto ahora es la integración de datos y su análisis. Así, la Biología de Sistemas emerge como una solución. Dónde los estudios genéticos una vez estimaban el impacto de un solo gen, ahora todos los datos disponibles para todos los genes se pueden integrar. Esto permite llegar a conclusiones más precisas, puesto que las enfermedades y las respuestas a fármacos están causadas por distintas combinaciones de perturbaciones genéticas. Incluso mejor, permite hacer simulaciones que de cualquier otro modo serían increíblemente costosas en términos de tiempo y recursos. En este contexto, se presenta aquí un nuevo método para integrar los datos disponibles para cada elemento de un camino de señalización en la actividad final resultante de dicho camino, el fenotipo. Este sistema sirve como un biomarcador mecanístico, puesto que el diferente nivel de activación que presente un camino, al comparar muestras, sirve para indicar mucha más información sobre los mecanismos que están funcionando de forma distinta. Un método mucho más informativo que los biomarcadores descriptivos. Además, el método permite realizar simulaciones. Al introducir información sobre los efectos de un fármaco, se puede modificar el nivel de actividad del camino y estimar si sus efectos son deseados. Los pacientes con cáncer a menudo no responden deseablemente a una terapia, un gran problema en la oncología que se piensa es debido a la falta de biomarcadores predictivos. La actividad de los caminos de señalización en células cancerígenas puede utilizarse como biomarcador mecanístico. Este proyecto pretende emplear esta nueva herramienta para el reposicionamiento de fármacos en pacientes con cáncer.Bailach Adsuara, A. (2017). Pathway Activity Analysis (PAA) as a new class of mechanistic biomarker to predict drug responses in drug repositioning for cancer patients. http://hdl.handle.net/10251/86415TFG

Challenges in biomedical data science: data-driven solutions to clinical questions

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific domains, the amount of data involved in each decision-making process has become overwhelming. The complexity of the phenomena under investigation and the scale of modern data collections has long superseded human analysis and insights potential

3D Printing of Dietary Products for the Management of Inborn Errors of Intermediary Metabolism in Pediatric Populations

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/The incidence of Inborn Error of Intermediary Metabolism (IEiM) diseases may be low, yet collectively, they impact approximately 6–10% of the global population, primarily affecting children. Precise treatment doses and strict adherence to prescribed diet and pharmacological treatment regimens are imperative to avert metabolic disturbances in patients. However, the existing dietary and pharmacological products suffer from poor palatability, posing challenges to patient adherence. Furthermore, frequent dose adjustments contingent on age and drug blood levels further complicate treatment. Semi-solid extrusion (SSE) 3D printing technology is currently under assessment as a pioneering method for crafting customized chewable dosage forms, surmounting the primary limitations prevalent in present therapies. This method offers a spectrum of advantages, including the flexibility to tailor patient-specific doses, excipients, and organoleptic properties. These elements are pivotal in ensuring the treatment’s efficacy, safety, and adherence. This comprehensive review presents the current landscape of available dietary products, diagnostic methods, therapeutic monitoring, and the latest advancements in SSE technology. It highlights the rationale underpinning their adoption while addressing regulatory aspects imperative for their seamless integration into clinical practice.Peer reviewe

High-throughput screening, next generation sequencing and machine learning: advanced methods in enzyme engineering

Enzyme engineering is an important biotechnological process capable of generating tailored biocatalysts for applications in industrial chemical conversion and biopharma. Typical enhancements sought in enzyme engineering and in vitro evolution campaigns include improved folding stability, catalytic activity, and/or substrate specificity. Despite significant progress in recent years in the areas of high-throughput screening and DNA sequencing, our ability to explore the vast space of functional enzyme sequences remains severely limited. Here, we review the currently available suite of modern methods for enzyme engineering, with a focus on novel readout systems based on enzyme cascades, and new approaches to reaction compartmentalization including single-cell hydrogel encapsulation techniques to achieve a genotype–phenotype link. We further summarize systematic scanning mutagenesis approaches and their merger with deep mutational scanning and massively parallel next-generation DNA sequencing technologies to generate mutability landscapes. Finally, we discuss the implementation of machine learning models for computational prediction of enzyme phenotypic fitness from sequence. This broad overview of current state-of-the-art approaches for enzyme engineering and evolution will aid newcomers and experienced researchers alike in identifying the important challenges that should be addressed to move the field forward

Using Artificial Intelligence to Predict the Discharge Performance of Cathode Materials for Lithium-ion Batteries Applications

A comprehensive understanding of the composition-structure-property relationships for doped cathode materials used in lithium-ion batteries remains lacking which delays the progress of developing new cathode materials. This thesis proposes that machine learning (ML) techniques can be used to predict the discharge capacities of the cathode materials whilst revealing these underlying relationships. To achieve this, the data for three different doped cathodes are curated from the publications, namely, the doped spinel cathode, LiMxMn2−xO4, the M-doped nickel- cobalt-manganese layered cathode, LiNixCoyMnzM1−x−y−zO2, and the carbon -coated and doped olivine cathode, C/LiM1M2PO4 (M1, M2 denote different metal ions). Several linear and non-linear ML models are trained with the data and compared for the power of predicting initial and higher cycle discharge capacity. Gradient boosting models have shown the best prediction power for predicting the initial and 20th cycle end discharge capacity of 102 doped spinel cathode and the initial and 50th cycle discharge capacity of 168 doped nickel-cobalt-manganese layered cathodes. For the doped spinel cathode, higher discharge capacities at both cycles can be achieved through increasing the material formula mass, reducing the crystal lattice constant and using dopants with smaller electronegativity. For the doped layered cathodes, it is revealed that the higher lithium content, lower formula molar mass, small doping content and doped with low electronegativity dopant are more likely to possess greater capacities at both cycles. Bayesian ridge regression and gradient boosting model are shown to have the highest prediction power over the initial and the 20th cycle discharge capacity of carbon-coated and doped olivine cathode. In addition, the olivine systems with lower dopant content, higher base-metal content and smaller unit cells are shown to be more likely to possess higher capacities at both cycles. Finally, future research directions are presented including the suggestion of involving other new input variables and using principal component analysis and feature selection algorithms to use to improve the model performance

Characterizing Signal Transduction Networks and Biological Responses Using Computer Simulations and Machine Learning

The use of computer simulations in biology is often limited due to the lack of experimentally measured parameters. In these scenarios, parameter exploration can be used to probe biological systems and refine understanding of biological mechanisms. For systems with few unknown parameters, parameter sweeps that concurrently vary all unknown parameters are tractable. In complex systems with many unknown parameters, supervised machine learning algorithms can be used to discover parameters leading to targeted system responses. In this thesis, we study three biological problems in which we use parameter exploration methods to gain mechanistic insights. We first explore the role of altered metabolism in cancer cells that reside in heterogeneous tumor microenvironments. We use a multiscale, hybrid cellular automaton model to evaluate tumor progression while varying malignant cell traits using a systematic parameter sweep. The results reveal distinct growth regimes associated with varied malignant cell traits. We then study kinetic mechanisms governing fixed-topology signal transduction networks and use evolutionary algorithms to discover kinetic parameters that produce specified network responses. We analyze the growth-response network in Arabidopsis with this supervised machine learning approach. This allows us to identify constraints on kinetic parameters that govern the observed responses. The evolved parameters are used to calculate the responses of individual network components, which are used to generate hypotheses that can be tested in vivo to help determine the network topology. We finally apply a similar approach to redesign signal transduction networks. We demonstrate that the T cell receptor network and an oscillator network show remarkable flexibility in generating altered responses to input, and we further use a nonlinear clustering method to identify design criteria for the underlying kinetic parameters. For each project, observations produced from in silico simulations lead to the formation of hypotheses that are experimentally testable

Using advanced computational methods to model the binding of antibody complexes: a case study from the coagulation cascade

Haemophilia A is a congenital bleeding disorder affecting one in 5,000 to 10,000 males. To prevent symptomatic disease, injections of recombinant factor VIII (FVIII) are administered to compensate for insufficient levels of this essential clotting factor. Patients suffering from a severe form of haemophilia A are at increased risk of forming neutralising antibodies — known as inhibitors — against therapeutic FVIII. A better understanding of the binding characteristics of inhibitors may aid the selection of optimal haemophilia A therapies, lead to the development of new therapeutics that are less antigenic, and support future initiatives in personalised and precision medicine. With this goal in mind, Classical Molecular Dynamics (CMD) in conjunction with Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) free energy calculations, together with enhanced sampling techniques, have been used to investigate interactions and the dynamics of binding site residues of the human inhibitory antibody BO2C11 bound to the C2-domain of factor VIII. In parallel, recombinant bacterial expressions of the C2-domain were initiated with the aim to explore structural changes induced by mutations that abrogate binding as described previously in surface plasmon resonance experiments. Computational binding affinity predictions were generally shown to be in good agreement with experimental findings. Additionally, binding site dynamics were investigated in detail using customized visualization techniques and an interpretable machine learning approach. Nevertheless, CMD simulations were insufficient for gaining insights into structural changes induced by mutations that were determined experimentally to be non-binding, and for exploring the underlying differences between the bound and unbound structures of the FVIII-C2 domain. To this end, Accelerated Molecular Dynamics (AMD) and Umbrella Sampling (US) simulations proved to be appropriate additions to investigate the conformational changes and energetic differences associated with the binding of BO2C11

Design of selective peptide inhibitors of anti-apoptotic Bfl-1 using experimental screening, structure-based design, and data-driven modeling

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Biology, 2018.Cataloged from PDF version of thesis.Includes bibliographical references.Protein-protein interactions are central to all biological processes. Designer reagents that selectively bind to proteins and inhibit their interactions can be used to probe protein interaction networks, discover druggable targets, and generate potential therapeutic leads. Current technology makes it possible to engineer proteins and peptides with desirable interaction profiles using carefully selected sets of experiments that are customized for each design objective. There is great interest in improving the protein design pipeline to create protein binders more efficiently and against a wider array of targets. In this thesis, I describe the design and development of selective peptide inhibitors of anti-apoptotic BcI-2 family proteins, with an emphasis on targeting Bfl-1. Anti-apoptotic Bcl-2 family proteins bind to short, pro-apoptotic BH3 motifs to support cellular survival. Overexpression of BfI-1 has been shown to promote cancer cell survival and the development of chemoresistance. Prior work suggests that selective inhibition of Bfl-1 can induce cell death in Bfl-1 overexpressing cancer cells without compromising healthy cells that also rely on anti-apoptotic BcI-2 proteins for survival. Thus, Bfl-1-selective BH3 mimetic peptides are potentially valuable for diagnosing Bfl-1 dependence and can serve as leads for therapeutic development. In this thesis, I describe three distinct approaches to designing potent and selective Bfl-1 inhibitors. First, I describe the design and screening of libraries of variants of BH3 peptides. I show that peptides from this screen bind in a previously unobserved BH3 binding mode and have large margins of specificity for Bfl-1 when tested in vitro and in cultured cells. Second, I describe a computational model of the specificity landscape of three anti-apoptotic Bcl-2 proteins including Bfl-1. This model was derived from high-throughput affinity measurement of thousands of peptides from BH3 libraries. I show that this model is useful for designing peptides with desirable interaction profiles within a family of related proteins. Third, I describe the use of a scoring potential built on the amino acid frequencies from well-defined structural motifs complied from the Protein Data Bank to design novel BH3 peptides targeting Bfl-1.by Justin Michael Jenson.Ph. D