Mechanochemical Approaches to Fundamental Studies in Soft-Matter Physics.

Stretching a segment of a polymer beyond its contour length makes its (primarily backbone) bonds more dissociatively labile, which enables polymer mechanochemistry. Integrating some backbone bonds into suitably designed molecular moieties yields mechanistically and kinetically diverse chemistry, which is becoming increasingly exploitable. Examples include, most prominently, attempts to improve mechanical properties of bulk polymers, as well as prospective applications in drug delivery and synthesis. This review aims to highlight an emerging effort to apply the concepts and experimental tools of mechanochemistry to fundamental physical questions in soft matter. A succinct summary of the state-of-the-knowledge of the field, with emphasis on foundational concepts and generalizable observations, is followed by analysis of 3 recent examples of mechanochemistry yielding molecular-level details of elastomer failure, macromolecular chain dynamics in elongational flows and kinetic allostery. We conclude with reasons to assume that the highlighted approaches are generalizable to a broader range of physical problems than considered to date.

Coumarin Dimer Is an Effective Photomechanochemical AND Gate for Small-Molecule Release.

Stimulus-responsive gating of chemical reactions is of considerable practical and conceptual interest. For example, photocleavable protective groups and gating mechanophores allow the kinetics of purely thermally activated reactions to be controlled optically or by mechanical load by inducing the release of small-molecule reactants. Such release only in response to a sequential application of both stimuli (photomechanochemical gating) has not been demonstrated despite its unique expected benefits. Here, we describe computational and experimental evidence that coumarin dimers are highly promising moieties for realizing photomechanochemical control of small-molecule release. Such dimers are transparent and photochemically inert at wavelengths >300 nm but can be made to dissociate rapidly under tensile force. The resulting coumarins are mechanochemically and thermally stable, but rapidly release their payload upon irradiation. Our DFT calculations reveal that both strain-free and mechanochemical kinetics of dimer dissociation are highly tunable over an unusually broad range of rates by simple substitution. In head-to-head dimers, the phenyl groups act as molecular levers to allow systematic and predictable variation in the force sensitivity of the dissociation barriers by choice of the pulling axis. As a proof-of-concept, we synthesized and characterized the reactivity of one such dimer for photomechanochemically controlled release of aniline and its application for controlling bulk gelation.

Sacrificial Mechanical Bond is as Effective as a Sacrificial Covalent Bond in Increasing Cross-Linked Polymer Toughness.

Sacrificial chemical bonds have been used effectively to increase the toughness of elastomers because such bonds dissociate at forces significantly below the fracture limit of the primary load-bearing bonds, thereby dissipating local stress. This approach owes much of its success to the ability to adjust the threshold force at which the sacrificial bonds fail at the desired rate, for example, by selecting either covalent or noncovalent sacrificial bonds. Here, we report experimental and computational evidence that a mechanical bond, responsible for the structural integrity of a rotaxane or a catenane, increases the elastomer's fracture strain, stress, and energy as much as a covalent bond of comparable mechanochemical dissociation kinetics. We synthesized and studied 6 polyacrylates cross-linked by either difluorenylsuccinonitrile (DFSN), which is an established sacrificial mechanochromic moiety; a [2]rotaxane, whose stopper allows its wheel to dethread on the same subsecond time scale as DFSN dissociates when either is under tensile force of 1.5-2 nN; a structurally homologous [2]rotaxane with a much bulkier stopper that is stable at force >5.5 nN; similarly stoppered [3]rotaxanes containing DFSN in their axles; and a control polymer with aliphatic nonsacrificial cross-links. Our data suggest that mechanochemical dethreading of a rotaxane without failure of any covalent bonds may be an important, hitherto unrecognized, contributor to the toughness of some rotaxane-cross-linked polymers and that sacrificial mechanical bonds provide a mechanism to control material fracture behavior independently of the mechanochemical response of the covalent networks, due to their distinct relationships between structure and mechanochemical reactivity.

Allosteric control of olefin isomerization kinetics via remote metal binding and its mechanochemical analysis.

Allosteric control of reaction thermodynamics is well understood, but the mechanisms by which changes in local geometries of receptor sites lower activation reaction barriers in electronically uncoupled, remote reaction moieties remain relatively unexplored. Here we report a molecular scaffold in which the rate of thermal E-to-Z isomerization of an alkene increases by a factor of as much as 104 in response to fast binding of a metal ion to a remote receptor site. A mechanochemical model of the olefin coupled to a compressive harmonic spring reproduces the observed acceleration quantitatively, adding the studied isomerization to the very few reactions demonstrated to be sensitive to extrinsic compressive force. The work validates experimentally the generalization of mechanochemical kinetics to compressive loads and demonstrates that the formalism of force-coupled reactivity offers a productive framework for the quantitative analysis of the molecular basis of allosteric control of reaction kinetics. Important differences in the effects of compressive vs. tensile force on the kinetic stabilities of molecules are discussed.

Experimental quantitation of molecular conditions responsible for flow-induced polymer mechanochemistry.

Fragmentation of macromolecular solutes in rapid flows is of considerable fundamental and practical importance. The sequence of molecular events preceding chain fracture is poorly understood, because such events cannot be visualized directly but must be inferred from changes in the bulk composition of the flowing solution. Here we describe how analysis of same-chain competition between fracture of a polystyrene chain and isomerization of a chromophore embedded in its backbone yields detailed characterization of the distribution of molecular geometries of mechanochemically reacting chains in sonicated solutions. In our experiments the overstretched (mechanically loaded) chain segment grew and drifted along the backbone on the same timescale as, and in competition with, the mechanochemical reactions. Consequently, only <30% of the backbone of a fragmenting chain is overstretched, with both the maximum force and the maximum reaction probabilities located away from the chain centre. We argue that quantifying intrachain competition is likely to be mechanistically informative for any flow fast enough to fracture polymer chains.

Selective ortho-C-H Activation in Arenes without Functional Groups.

Aromatic C-H activation in alkylarenes is a key step for the synthesis of functionalized organic molecules from simple hydrocarbon precursors. Known examples of such C-H activations often yield mixtures of products resulting from activation of the least hindered C-H bonds. Here we report highly selective ortho-C-H activation in alkylarenes by simple iridium complexes. We demonstrate that the capacity of the alkyl substituent to override the typical preference of metal-mediated C-H activation for the least hindered aromatic C-H bonds results from transient insertion of iridium into the benzylic C-H bond. This enables fast iridium insertion into the ortho-C-H bond, followed by regeneration of the benzylic C-H bond by reductive elimination. Bulkier alkyl substituents increase the ortho selectivity. The described chemistry represents a conceptually new alternative to existing approaches for aromatic C-H bond activation.

The many flavours of mechanochemistry and its plausible conceptual underpinnings.

Mechanochemistry describes diverse phenomena in which mechanical load affects chemical reactivity. The fuzziness of this definition means that it includes processes as seemingly disparate as motor protein function, organic synthesis in a ball mill, reactions at a propagating crack, chemical actuation, and polymer fragmentation in fast solvent flows and in mastication. In chemistry, the rate of a reaction in a flask does not depend on how fast the flask moves in space. In mechanochemistry, the rate at which a material is deformed affects which and how many bonds break. In other words, in some manifestations of mechanochemistry, macroscopic motion powers otherwise endergonic reactions. In others, spontaneous chemical reactions drive mechanical motion. Neither requires thermal or electrostatic gradients. Distinct manifestations of mechanochemistry are conventionally treated as being conceptually independent, which slows the field in its transformation from being a collection of observations to a rigorous discipline. In this Review, we highlight observations suggesting that the unifying feature of mechanochemical phenomena may be the coupling between inertial motion at the microscale to macroscale and changes in chemical bonding enabled by transient build-up and relaxation of strains, from macroscopic to molecular. This dynamic coupling across multiple length scales and timescales also greatly complicates the conceptual understanding of mechanochemistry.

Reacting to crises: The COVID-19 impact on biostatistics/epidemiology.

Most crises, though difficult and challenging to address, offer opportunities for change and for development of new perspectives or approaches to deal with traditional strategies. The reaction to and the managing of the COVID-19 pandemic has provided a platform for evaluating how we quantify disease prevalence, incidence, time courses and sequellae as well as how well we plan, design, analyze and interpret health care associated data, including clinical trials and electronic medical records and health claims data. Whether the Covid-19 crisis provides opportunities to advance the fields of biostatistics and epidemiology in select ways remains to be seen. This article describes three areas of crises experienced by the author during a career in the regulation of pharmaceutical products and how they were responded to. Some suggestions for potential future opportunities in reaction to the Covid-19 crises are provided.

CTSA Consortium Consensus Scientific Review Committee (SRC) Working Group Report on the SRC Processes.

Human research projects must have a scientifically valid study design, analytic plan, and be operationally feasible in order to be successfully completed and thus to have translational impact. To ensure this, institutions that conduct clinical research should have a scientific review process prior to submission to the Institutional Review Committee (IRB). This paper reports the Clinical and Translational Science Award (CTSA) Consortium Scientific Review Committee (SRC) Consensus Working Group's proposed framework for a SRC process. Recommendations are provided for institutional support and roles of CTSAs, multisite research, criteria for selection of protocols that should be reviewed, roles of committee members, application process, and committee process. Additionally, to support the SCR process effectively, and to ensure efficiency, the Working Group recommends information technology infrastructures and evaluation metrics to determine outcomes are provided.

Impacts on type I error rate with inappropriate use of learn and confirm in confirmatory adaptive design trials.

A two-stage adaptive design trial is a single trial that combines the learning data from stage 1 (or phase II) and the confirming data in stage 2 (or phase III) for formal statistical testing. We call it a "Learn and Confirm" trial. The studywise type I error rate remains to be at issue in a "Learn and Confirm" trial. For studying multiple doses or multiple enpdoints, a "Learn and Confirm" adaptive design can be more attractive than a fixed design approach. This is because intuitively the learning data in stage 1 should not be subjected to type I error scrutiny if there is no formal interim analysis performed and only an adaptive selection of design parameters is made at stage 1. In this work, we conclude from extensive simulation studies that the intuition is most often misleading. That is, regardless of whether or not there is a formal interim analysis for making an adaptive selection, the type I error rates are always at risk of inflation. Inappropriate use of any "Learn and Confirm" strategy should not be overlooked.

Consideration of regional difference in design and analysis of multi-regional trials.

Clinical trial strategy, particularly in developing pharmaceutical products, has recently expanded to a global level in the sense that multiple geographical regions participate in the trial simultaneously under the same study protocol. The possible benefits of this strategy are obvious, at least from the cost and efficiency considerations. The challenges with this strategy are many, ranging from trial or data quality assurance to statistical methods for design and analysis of such trials. In many regulatory submissions, the presence of regional differences in the estimated treatment effect, whether they are different only in magnitude or in direction, often presents great difficulty in interpretation of the global trial results, particularly for the acceptability by the local regulatory authorities. This article presents a number of useful statistical analysis tools for exploration of regional differences and a method that may be worth consideration in designing a multi-regional clinical trial.

Statistical considerations in evaluating pharmacogenomics-based clinical effect for confirmatory trials.

BACKGROUND: The current practice for seeking genomically favorable patients in randomized controlled clinical trials using genomic convenience samples. PURPOSE: To discuss the extent of imbalance, confounding, bias, design efficiency loss, type I error, and type II error that can occur in the evaluation of the convenience samples, particularly when they are small samples. To articulate statistical considerations for a reasonable sample size to minimize the chance of imbalance, and, to highlight the importance of replicating the subgroup finding in independent studies. METHODS: Four case examples reflecting recent regulatory experiences are used to underscore the problems with convenience samples. Probability of imbalance for a pre-specified subgroup is provided to elucidate sample size needed to minimize the chance of imbalance. We use an example drug development to highlight the level of scientific rigor needed, with evidence replicated for a pre-specified subgroup claim. RESULTS: The convenience samples evaluated ranged from 18% to 38% of the intent-to-treat samples with sample size ranging from 100 to 5000 patients per arm. The baseline imbalance can occur with probability higher than 25%. Mild to moderate multiple confounders yielding the same directional bias in favor of the treated group can make treatment group incomparable at baseline and result in a false positive conclusion that there is a treatment difference. Conversely, if the same directional bias favors the placebo group or there is loss in design efficiency, the type II error can increase substantially. LIMITATIONS: Pre-specification of a genomic subgroup hypothesis is useful only for some degree of type I error control. CONCLUSION: Complete ascertainment of genomic samples in a randomized controlled trial should be the first step to explore if a favorable genomic patient subgroup suggests a treatment effect when there is no clear prior knowledge and understanding about how the mechanism of a drug target affects the clinical outcome of interest. When stratified randomization based on genomic biomarker status cannot be implemented in designing a pharmacogenomics confirmatory clinical trial, if there is one genomic biomarker prognostic for clinical response, as a general rule of thumb, a sample size of at least 100 patients may be needed to be considered for the lower prevalence genomic subgroup to minimize the chance of an imbalance of 20% or more difference in the prevalence of the genomic marker. The sample size may need to be at least 150, 350, and 1350, respectively, if an imbalance of 15%, 10% and 5% difference is of concern.

Adaptive patient enrichment designs in therapeutic trials.

The utility of clinical trial designs with adaptive patient enrichment is investigated in an adequate and well-controlled trial setting. The overall treatment effect is the weighted average of the treatment effects in the mutually exclusive subsets of the originally intended entire study population. The adaptive enrichment approaches permit assessment of treatment effect that may be applicable to specific nested patient (sub)sets due to heterogeneous patient characteristics and/or differential response to treatment, e.g. a responsive patient subset versus a lack of beneficial patient subset, in all patient (sub)sets studied. The adaptive enrichment approaches considered include three adaptive design scenarios: (i) total sample size fixed and with futility stopping, (ii) sample size adaptation and futility stopping, and (iii) sample size adaptation without futility stopping. We show that regardless of whether the treatment effect eventually assessed is applicable to the originally studied patient population or only to the nested patient subsets; it is possible to devise an adaptive enrichment approach that statistically outperforms one-size-fits-all fixed design approach and the fixed design with a pre-specified multiple test procedure. We emphasize the need of additional studies to replicate the finding of a treatment effect in an enriched patient subset. The replication studies are likely to need fewer number of patients because of an identified treatment effect size that is larger than the diluted overall effect size. The adaptive designs, when applicable, are along the line of efficiency consideration in a drug development program.

Approaches to evaluation of treatment effect in randomized clinical trials with genomic subset.

With the advances in human genomic/genetic studies, the clinical trial community gradually recognizes that phenotypically homogeneous patients may be heterogeneous at the genomic level. The genomic technology brings a possible avenue for developing a genomic (composite) biomarker to predict a genomically responsive patient subset that may have a (much) higher likelihood of benefiting from a treatment. Randomized controlled trial is the mainstay to provide scientifically convincing evidence of a purported effect a new treatment may demonstrate. In conventional clinical trials, the primary clinical hypothesis pertains to the therapeutic effect in all patients who are eligible for the study defined by the primary efficacy endpoint. The aspect of one-size-fits-all surrounding the conventional design has been challenged, particularly when the diseases may be heterogeneous due to observable clinical characteristics and/or unobservable underlying the genomic characteristics. Extension from the conventional single population design objective to an objective that encompasses two possible patient populations will allow more informative evaluation in the patients having different degrees of responsiveness to medication. Building in conventional clinical trials, an additional genomic objective can generate an appealing conceptual framework from the patient's perspective in addressing personalized medicine in well-controlled clinical trials. There are many perceived benefits of personalized medicine that are based on the notion of being genomically proactive in the identification of disease and prevention of disease or recurrence. In this paper, we show that an adaptive design approach can be constructed to study a clinical hypothesis of overall treatment effect and a hypothesis of treatment effect in a genomic subset more efficiently than the conventional non-adaptive approach.

Adapting the sample size planning of a phase III trial based on phase II data.

Traditionally, in clinical development plan, phase II trials are relatively small and can be expected to result in a large degree of uncertainty in the estimates based on which Phase III trials are planned. Phase II trials are also to explore appropriate primary efficacy endpoint(s) or patient populations. When the biology of the disease and pathogenesis of disease progression are well understood, the phase II and phase III studies may be performed in the same patient population with the same primary endpoint, e.g. efficacy measured by HbA1c in non-insulin dependent diabetes mellitus trials with treatment duration of at least three months. In the disease areas that molecular pathways are not well established or the clinical outcome endpoint may not be observed in a short-term study, e.g. mortality in cancer or AIDS trials, the treatment effect may be postulated through use of intermediate surrogate endpoint in phase II trials. However, in many cases, we generally explore the appropriate clinical endpoint in the phase II trials. An important question is how much of the effect observed in the surrogate endpoint in the phase II study can be translated into the clinical effect in the phase III trial. Another question is how much of the uncertainty remains in phase III trials. In this work, we study the utility of adaptation by design (not by statistical test) in the sense of adapting the phase II information for planning the phase III trials. That is, we investigate the impact of using various phase II effect size estimates on the sample size planning for phase III trials. In general, if the point estimate of the phase II trial is used for planning, it is advisable to size the phase III trial by choosing a smaller alpha level or a higher power level. The adaptation via using the lower limit of the one standard deviation confidence interval from the phase II trial appears to be a reasonable choice since it balances well between the empirical power of the launched trials and the proportion of trials not launched if a threshold lower than the true effect size of phase III trial can be chosen for determining whether the phase III trial is to be launched.

Methodological issues with adaptation of clinical trial design.

Adaptation of clinical trial design generates many issues that have not been resolved for practical applications, though statistical methodology has advanced greatly. This paper focuses on some methodological issues. In one type of adaptation such as sample size re-estimation, only the postulated value of a parameter for planning the trial size may be altered. In another type, the originally intended hypothesis for testing may be modified using the internal data accumulated at an interim time of the trial, such as changing the primary endpoint and dropping a treatment arm. For sample size re-estimation, we make a contrast between an adaptive test weighting the two-stage test statistics with the statistical information given by the original design and the original sample mean test with a properly corrected critical value. We point out the difficulty in planning a confirmatory trial based on the crude information generated by exploratory trials. In regards to selecting a primary endpoint, we argue that the selection process that allows switching from one endpoint to the other with the internal data of the trial is not very likely to gain a power advantage over the simple process of selecting one from the two endpoints by testing them with an equal split of alpha (Bonferroni adjustment). For dropping a treatment arm, distributing the remaining sample size of the discontinued arm to other treatment arms can substantially improve the statistical power of identifying a superior treatment arm in the design. A common difficult methodological issue is that of how to select an adaptation rule in the trial planning stage. Pre-specification of the adaptation rule is important for the practicality consideration. Changing the originally intended hypothesis for testing with the internal data generates great concerns to clinical trial researchers.

A regulatory view on adaptive/flexible clinical trial design.

Recently there is growing interest in use of adaptive or flexible designs for development of pharmaceutical products. Statistical methodology has been greatly advanced in the literature. However, there are still some important issues with the methodology and application. In addition, there are many other challenges with these designs, including efficiency of these designs in the entire development program, trial conduct and logistics, the infrastructure of an adaptive trial, the regulatory evaluation of trial results and trial conduct, etc. Up till now, regulatory experience in these designs is very limited. We share some of the challenges.

Multicentre trials: a US regulatory perspective.

Multicentre trials are very common in the field of drug development. In recent years, multicentre trials have taken on a multinational and multiregional aspect. We provide a conceptual framework for the use of multicentre trials in the context of drug development, from the perspective of drug regulation in the United States. In this paper, we review some regulatory history, milestones and standards as they relate to multicentre trials. Special attention is given to the similarities and differences in the approaches to multicentre trials in the following documents; Guideline for the Format and Content of the Clinical and Statistical Sections of New Drug Applications, International Conference on Harmonization, Draft Guideline on Statistical Principles for clinical trials and the Guidance for Industry Providing Clinical Evidence of Effectiveness for Human Drug and Biologic Products. The paper includes a consideration of some of the issues in the analysis of data from multicentre trials.