Advancing cancer drug development with mechanistic mathematical modeling: bridging the gap between theory and practice.

Quantitative predictive modeling of cancer growth, progression, and individual response to therapy is a rapidly growing field. Researchers from mathematical modeling, systems biology, pharmaceutical industry, and regulatory bodies, are collaboratively working on predictive models that could be applied for drug development and, ultimately, the clinical management of cancer patients. A plethora of modeling paradigms and approaches have emerged, making it challenging to compile a comprehensive review across all subdisciplines. It is therefore critical to gauge fundamental design aspects against requirements, and weigh opportunities and limitations of the different model types. In this review, we discuss three fundamental types of cancer models: space-structured models, ecological models, and immune system focused models. For each type, it is our goal to illustrate which mechanisms contribute to variability and heterogeneity in cancer growth and response, so that the appropriate architecture and complexity of a new model becomes clearer. We present the main features addressed by each of the three exemplary modeling types through a subjective collection of literature and illustrative exercises to facilitate inspiration and exchange, with a focus on providing a didactic rather than exhaustive overview. We close by imagining a future multi-scale model design to impact critical decisions in oncology drug development.

A quantitative systems pharmacology workflow toward optimal design and biomarker stratification of atopic dermatitis clinical trials.

BACKGROUND: The development of atopic dermatitis (AD) drugs is challenged by many disease phenotypes and trial design options, which are hard to explore experimentally. OBJECTIVE: We aimed to optimize AD trial design using simulations. METHODS: We constructed a quantitative systems pharmacology model of AD and standard of care (SoC) treatments and generated a phenotypically diverse virtual population whose parameter distribution was derived from known relationships between AD biomarkers and disease severity and calibrated using disease severity evolution under SoC regimens. RESULTS: We applied this workflow to the immunomodulator OM-85, currently being investigated for its potential use in AD, and calibrated the investigational treatment model with the efficacy profile of an existing trial (thereby enriching it with plausible marker levels and dynamics). We assessed the sensitivity of trial outcomes to trial protocol and found that for this particular example the choice of end point is more important than the choice of dosing regimen and patient selection by model-based responder enrichment could increase the expected effect size. A global sensitivity analysis revealed that only a limited subset of baseline biomarkers is needed to predict the drug response of the full virtual population. CONCLUSIONS: This AD quantitative systems pharmacology workflow built around knowledge of marker-severity relationships as well as SoC efficacy can be tailored to specific development cases to optimize several trial protocol parameters and biomarker stratification and therefore has promise to become a powerful model-informed AD drug development and personalized medicine tool.

Modeling the disruption of respiratory disease clinical trials by non-pharmaceutical COVID-19 interventions.

Respiratory disease trials are profoundly affected by non-pharmaceutical interventions (NPIs) against COVID-19 because they perturb existing regular patterns of all seasonal viral epidemics. To address trial design with such uncertainty, we developed an epidemiological model of respiratory tract infection (RTI) coupled to a mechanistic description of viral RTI episodes. We explored the impact of reduced viral transmission (mimicking NPIs) using a virtual population and in silico trials for the bacterial lysate OM-85 as prophylaxis for RTI. Ratio-based efficacy metrics are only impacted under strict lockdown whereas absolute benefit already is with intermediate NPIs (eg. mask-wearing). Consequently, despite NPI, trials may meet their relative efficacy endpoints (provided recruitment hurdles can be overcome) but are difficult to assess with respect to clinical relevance. These results advocate to report a variety of metrics for benefit assessment, to use adaptive trial design and adapted statistical analyses. They also question eligibility criteria misaligned with the actual disease burden.

Solving the Evidence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models?

Health technology assessment (HTA) aims to be a systematic, transparent, unbiased synthesis of clinical efficacy, safety, and value of medical products (MPs) to help policymakers, payers, clinicians, and industry to make informed decisions. The evidence available for HTA has gaps-impeding timely prediction of the individual long-term effect in real clinical practice. Also, appraisal of an MP needs cross-stakeholder communication and engagement. Both aspects may benefit from extended use of modeling and simulation. Modeling is used in HTA for data-synthesis and health-economic projections. In parallel, regulatory consideration of model informed drug development (MIDD) has brought attention to mechanistic modeling techniques that could in fact be relevant for HTA. The ability to extrapolate and generate personalized predictions renders the mechanistic MIDD approaches suitable to support translation between clinical trial data into real-world evidence. In this perspective, we therefore discuss concrete examples of how mechanistic models could address HTA-related questions. We shed light on different stakeholder's contributions and needs in the appraisal phase and suggest how mechanistic modeling strategies and reporting can contribute to this effort. There are still barriers dissecting the HTA space and the clinical development space with regard to modeling: lack of an adapted model validation framework for decision-making process, inconsistent and unclear support by stakeholders, limited generalizable use cases, and absence of appropriate incentives. To address this challenge, we suggest to intensify the collaboration between competent authorities, drug developers and modelers with the aim to implement mechanistic models central in the evidence generation, synthesis, and appraisal of HTA so that the totality of mechanistic and clinical evidence can be leveraged by all relevant stakeholders.

Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility.

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

Phenyl argentate aggregates [AgnPhn+1]- (n = 2-8): Models for the self-assembly of atom-precise polynuclear organometallics.

Electrospray ionization of phenyl argentates formed by transmetalation reactions between phenyl lithium and silver cyanide provides access to the argentate aggregates, [AgnPhn+1]-, which were individually mass-selected for n = 2-8 in order to generate their gas-phase Ultraviolet Photodissociation (UVPD) "action" spectra over the range 304-399 nm. A strong bathochromic shift in optical spectra was observed with increasing size/n. Theoretical calculations allowed the assignment of the experimental UVPD spectra to specific isomer(s) and provided crucial insights into the transition from the 2D to 3D structure of the metallic component with the increasing size of the complex. The [AgnPhn+1]- aggregates contain neither pronounced metallic cluster properties nor ligated metallic cluster features and are thus not superatom complexes. They therefore represent novel organometallic characteristics built from Ag2Ph subunits.

Possible Contexts of Use for In Silico Trials Methodologies: A Consensus-Based Review.

The term "In Silico Trial" indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Experimental and theoretical 2p core-level spectra of size-selected gas-phase aluminum and silicon cluster cations: chemical shifts, geometric structure, and coordination-dependent screening.

We present 2p core-level spectra of size-selected aluminum and silicon cluster cations from soft X-ray photoionization efficiency curves and density functional theory. The experimental and theoretical results are in very good quantitative agreement and allow for geometric structure determination. New ground state geometries for Al12+, Si15+, Si16+, and Si19+ are proposed on this basis. The chemical shifts of the 2p electron binding energies reveal a substantial difference for aluminum and silicon clusters: while in aluminum the 2p electron binding energy decreases with increasing coordination number, no such correlation was observed for silicon. The 2p binding energy shifts in clusters of both elements differ strongly from those of the corresponding bulk matter. For aluminum clusters, the core-level shifts between outer shell atoms and the encapsulated atom are of opposite sign and one order of magnitude larger than the corresponding core-level shift between surface and bulk atoms in the solid. For silicon clusters, the core-level shifts are of the same order of magnitude in clusters and in bulk silicon but no obvious correlation of chemical shift and bond length, as present for reconstructed silicon surfaces, are observed.

Ion mobility resolved photo-fragmentation to discriminate protomers.

RATIONALE: Among the sources of structural diversity in biomolecular ions, the co-existence of protomers is particularly difficult to take into account, which in turn complicates structural interpretation of gas-phase data. METHODS: We investigated the sensitivity of gas-phase photo-fragmentation measurements and ion mobility spectrometry (IMS) to the protonation state of a model peptide derivatized with chromophores. Accessible interconversion pathways between the different identified conformers were probed by tandem ion mobility measurement. Furthermore, the excitation coupling between the chromophores has been probed through photo-fragmentation measurements on mobility-selected ions. All results were interpreted based on molecular dynamics simulations. RESULTS: We show that protonation can significantly affect the photo-fragmentation yields. Especially, conformers with very close collision cross sections (CCSs) may display dramatically different photo-fragmentation yields in relation with different protonation patterns. CONCLUSIONS: We show that, even if precise structure assignment based on molecular modeling is in principle difficult for large biomolecular assemblies, the combination of photo-fragmentation and IMS can help to identify the signature of protomer co-existence for a population of biomolecular ions in the gas phase. Such spectroscopic data are particularly suitable to follow conformational changes.

Bringing Molecular Dynamics and Ion-Mobility Spectrometry Closer Together: Shape Correlations, Structure-Based Predictors, and Dissociation.

Unfolding of proteins gives detailed information about their structure and energetics and can be probed as a response to a change of experimental conditions. Ion mobility coupled to native mass spectrometry is a gas-phase technique that can observe such unfolding in the gas phase by monitoring the collision cross section (CCS) after applying an activation, for example, by collisions (collision-induced unfolding, CIU). The structural assignments needed to interpret the experiments can profit from dedicated modeling strategies. While predictions of ion-mobility data for well-defined and structurally characterized systems is straightforward, systematic free-energy calculations or biased molecular dynamics simulations that employ IMS data are still limited. The methods with which CCS values are calculated so far do not allow for analytical gradients needed in biased molecular dynamics (MD), and further, explicit CCS calculations still can pose computational bottleneck-when integrated into MD-bioinformatics workflows. These limitations motivate one to revisit known correlations of the CCS with the aim to find computationally cheap and versatile but still at least semiquantitative descriptions of the CCS by pure structural descriptors. We have therefore investigated the correlation of CCS with the key structural parameter often used in computational unfolding studies-the gyration radius-for several small monomeric and dimeric proteins. We work out the challenges and caveats of the combinations of the configurational sampling method and the CCS-calculation algorithm. The correlations were found to be sensitive to the generation conditions and additionally to the system topology. To reduce the amount of fitting to be undertaken, we devise a simple structural model for the CCS that shares some commonalities with the hard-sphere model and the projection algorithm but is designed to take unfolding into account. With this model, we suggest a two-point interpolating function rather than fitting a large data set, at only little deterioration of the predictive power. We further proceed to a model with composition and structure dependence that builds only upon the gyration radius and the chemical formula to apply the found CCS scaling behavior-the scaled macroscopic sphere (sMS) predictor. We demonstrate its applicability to describe unfolding and also its transferability for a larger set of structures from the RSCPDB. As we have found for the dimeric systems, that shape correlations with one global descriptor qualitatively break down, we finally suggest a recipe to switch between global and fragment-based CCS prediction, that takes up the ideas of coarse-graining protein complexes. The presented models and approaches might provide a basis to boost the integration of structural modeling with multistage IMS experiments, especially in the field of large-scale bioinformatics or "on-the-fly" biasing of MD, where computational efficiency is critical.

Visible Multiphoton Dissociation of Chromophore-Tagged Peptides.

The visible photodissociation mechanisms of QSY7-tagged peptides of increasing size have been investigated by coupling a mass spectrometer and an optical parametric oscillator laser beam. The experiments herein consist of energy resolved collision- and laser-induced dissociation measurements on the chromophore-tagged peptides. The results show that fragmentation occurs by similar channels in both activation methods, but that the branching ratios are vastly different. Observation of a size-dependent minimum laser pulse energy required to induce fragmentation, and collisional cooling rates in time resolved experiments show that laser-induced dissociation occurs through the absorption of multiple photons by the chromophore and the subsequent heating through vibrational energy redistribution. The differences in branching ratio between collision- and laser-induced dissociation can then be understood by the highly anisotropic energy distribution following absorption of a photon. Graphical Abstract ᅟ.

Correction: Supramolecular influence on cis-trans isomerization probed by ion mobility spectrometry.

Correction for 'Supramolecular influence on cis-trans isomerization probed by ion mobility spectrometry' by Izabella Czerwinska et al., Phys. Chem. Chem. Phys., 2016, 18, 32331-32336.

Dimerization and conformation-related free energy landscapes of dye-tagged amyloid-ß12-28 linked to FRET experiments.

We have investigated the free energy landscape of Aß-peptide dimer models in connection to gas-phase FRET experiments. We use a FRET-related distance coordinate and one conformation-related coordinate per monomer for accelerated structural exploration with well-tempered metadynamics in solvent and in vacuo. The free energy profiles indicate that FRET under equilibrium conditions should be significantly affected by the de-solvation upon the transfer of ions to the gas-phase. In contrast, a change in the protonation state is found to be less impacting once de-solvated. Comparing F19P and WT alloforms, for which we measure different FRET efficiencies in the gas-phase, we predict only the relevant structural differences in the solution populations, not under gas-phase equilibrium conditions. This finding supports the hypothesis that the gas-phase action-FRET measurement after ESI operates under non-equilibrium conditions, with a memory of the solution conditions - even for the dimer of this relatively short peptide. The structural differences in solution are rationalized in terms of conformational propensities around residue 19, which show a transition to a poly-proline type of pattern upon mutation to F19P - a difference that gets lost in the gas-phase.

Action-FRET of a Gaseous Protein.

Mass spectrometry is an extremely powerful technique for analysis of biological molecules, in particular proteins. One aspect that has been contentious is how much native solution-phase structure is preserved upon transposition to the gas phase by soft ionization methods such as electrospray ionization. To address this question-and thus further develop mass spectrometry as a tool for structural biology-structure-sensitive techniques must be developed to probe the gas-phase conformations of proteins. Here, we report Förster resonance energy transfer (FRET) measurements on a ubiquitin mutant using specific photofragmentation as a reporter of the FRET efficiency. The FRET data is interpreted in the context of circular dichroism, molecular dynamics simulation, and ion mobility data. Both the dependence of the FRET efficiency on the charge state-where a systematic decrease is observed-and on methanol concentration are considered. In the latter case, a decrease in FRET efficiency with methanol concentration is taken as evidence that the conformational ensemble of gaseous protein cations retains a memory of the solution phase conformational ensemble upon electrospray ionization. Graphical Abstract ᅟ.

Supramolecular influence on cis-trans isomerization probed by ion mobility spectrometry.

We used tandem ion mobility spectrometry measurements to investigate how the photo-isomerization of a chromophore (a methylpyridinium derivative) is affected by the complexation with a crown ether. A dramatic blue-shift of the photo-isomerization spectrum was observed upon complexation, which could be well reproduced by ab initio calculations. Our results support that the observed changes in the photo-physical properties of the chromophore originate from the charge-solvation of its pyridinium moiety by the host cage.

Excited States of Xanthene Analogues: Photofragmentation and Calculations by CC2 and Time-Dependent Density Functional Theory.

Action spectroscopy has emerged as an analytical tool to probe excited states in the gas phase. Although comparison of gas-phase absorption properties with quantum-chemical calculations is, in principle, straightforward, popular methods often fail to describe many molecules of interest-such as xanthene analogues. We, therefore, face their nano- and picosecond laser-induced photofragmentation with excited-state computations by using the CC2 method and time-dependent density functional theory (TDDFT). Whereas the extracted absorption maxima agree with CC2 predictions, the TDDFT excitation energies are blueshifted. Lowering the amount of Hartree-Fock exchange in the DFT functional can reduce this shift but at the cost of changing the nature of the excited state. Additional bandwidth observed in the photofragmentation spectra is rationalized in terms of multiphoton processes. Observed fragmentation from higher-lying excited states conforms to intense excited-to-excited state transitions calculated with CC2. The CC2 method is thus suitable for the comparison with photofragmentation in xanthene analogues.

The Gas-Phase Photophysics of Eosin Y and its Maleimide Conjugate.

The use of the xanthene family of dyes as fluorescent probes in a wide range of applications has provided impetus for the studying of their photophysical properties. In particular, recent advances in gas-phase techniques such as FRET that utilize such chromophores have placed a greater importance on the characterization of these properties in the gas phase. Additionally, the use of synthetic linker chains to graft the chromophores in a site-specific manner to their target system is ubiquitous. There is, however, often limited information on how the addition of such a linker chain may affect the photophysical properties of the chromophores, which is of fundamental importance for interpretation of experimental data reliant on grafted chromophores. Here, we present data on the optical spectroscopy of different protonation states of Eosin Y, a fluorescein derivative. We compare the photophysics of Eosin Y to its maleimide conjugate, and to the thioether product of the reaction of this conjugate with cysteamine. Comparison of the mass spectra following laser irradiation shows that very different relaxation takes place upon addition of the maleimide moiety but that the photophysics of the bare chromophore are restored upon addition of cysteamine. This radical change in the photophysics is interpreted in terms of charge-transfer states, whose energy relative to the S1 ← S0 transition of the chromophore is dependent on the conjugation of the maleimide moiety. We also show that the shape of the absorption band is unchanged in the gas-phase as compared to the solution-phase, showing a maximum with a shoulder toward the blue, and examination of isotope distributions of the isolated ions show that this shoulder cannot be due to the presence of dimers. Consideration of the fluorescence emission spectrum allows a tentative assignment of the shoulder to be due to a vibrational progression with a high Franck-Condon factor.

Sequential Proton Coupled Electron Transfer (PCET): Dynamics Observed over 8 Orders of Magnitude in Time.

Charge transfer mechanisms lay at the heart of chemistry and biochemistry. Proton coupled electron transfers (PCET) are central in biological processes such as photosynthesis and in the respiratory chain, where they mediate long-range charge transfers. These mechanisms are normally difficult to harness experimentally due to the intrinsic complexity of the associated biological systems. Metal-peptide cations experience both electron and proton transfers upon photoexcitation, proving an amenable model system to study PCET. We report on a time-resolved experiment designed to follow this dual charge transfer kinetics in [HG3W+Ag](+) (H = histidine, G = glycine, W = tryptophan) on time scales ranging from femtoseconds to milliseconds. While electron transfer completes in less than 4 ps, it triggers a proton transfer lasting over hundreds of microseconds. Molecular dynamics simulations show that conformational dynamic plays an important role in slowing down this reaction. This combined experimental and computational approach provides a view of PCET as a single phenomenon despite its very wide time-domain span.