Predicting crystal form stability under real-world conditions.

The physicochemical properties of molecular crystals, such as solubility, stability, compactability, melting behaviour and bioavailability, depend on their crystal form1. In silico crystal form selection has recently come much closer to realization because of the development of accurate and affordable free-energy calculations2-4. Here we redefine the state of the art, primarily by improving the accuracy of free-energy calculations, constructing a reliable experimental benchmark for solid-solid free-energy differences, quantifying statistical errors for the computed free energies and placing both hydrate crystal structures of different stoichiometries and anhydrate crystal structures on the same energy landscape, with defined error bars, as a function of temperature and relative humidity. The calculated free energies have standard errors of 1-2 kJ mol-1 for industrially relevant compounds, and the method to place crystal structures with different hydrate stoichiometries on the same energy landscape can be extended to other multi-component systems, including solvates. These contributions reduce the gap between the needs of the experimentalist and the capabilities of modern computational tools, transforming crystal structure prediction into a more reliable and actionable procedure that can be used in combination with experimental evidence to direct crystal form selection and establish control5.

A data-driven and topological mapping approach for the a priori prediction of stable molecular crystalline hydrates.

Predictions of the structures of stoichiometric, fractional, or nonstoichiometric hydrates of organic molecular crystals are immensely challenging due to the extensive search space of different water contents, host molecular placements throughout the crystal, and internal molecular conformations. However, the dry frameworks of these hydrates, especially for nonstoichiometric or isostructural dehydrates, can often be predicted from a standard anhydrous crystal structure prediction (CSP) protocol. Inspired by developments in the field of drug binding, we introduce an efficient data-driven and topologically aware approach for predicting organic molecular crystal hydrate structures through a mapping of water positions within the crystal structure. The method does not require a priori specification of water content and can, therefore, predict stoichiometric, fractional, and nonstoichiometric hydrate structures. This approach, which we term a mapping approach for crystal hydrates (MACH), establishes a set of rules for systematic determination of favorable positions for water insertion within predicted or experimental crystal structures based on considerations of the chemical features of local environments and void regions. The proposed approach is tested on hydrates of three pharmaceutically relevant compounds that exhibit diverse crystal packing motifs and void environments characteristic of hydrate structures. Overall, we show that our mapping approach introduces an advance in the efficient performance of hydrate CSP through generation of stable hydrate stoichiometries at low cost and should be considered an integral component for CSP workflows.

Non-inferiority analysis of subcutaneous versus intravenous 300 mg monthly natalizumab administration: A post hoc analysis of the REFINE study.

To quantify the probability that monthly intravenous (IV) and subcutaneous (SC) natalizumab (NTZ) had similar efficacy in relapsing-remitting multiple sclerosis (RRMS), non-inferiority of efficacy of NTZ-SC versus NTZ-IV on combined MRI unique active lesions number (CUAL) was explored re-analysing the REFINE data set. Non-inferiority margins were selected equal to 25%/33%/50% fractions of the effect size of NTZ-IV versus placebo observed in the AFFIRM study. Ninety-nine RRMS were included. NTZ-SC resulted not inferior to NTZ-IV on CUAL for all margins at 2.5% significance level, and, in worst-case scenario, its effect over NTZ-IV did not exceed 3.5% (or 2.8%) of the effect of NTZ-IV versus placebo.

Autologous haematopoietic stem cell transplantation versus low-dose immunosuppression in secondary-progressive multiple sclerosis.

BACKGROUND AND PURPOSE: Effectiveness of autologous haematopoietic stem cell transplantation (AHSCT) in relapsing-remitting multiple sclerosis (MS) is well known, but in secondary-progressive (SP)-MS it is still controversial. Therefore, AHSCT activity was evaluated in SP-MS using low-dose immunosuppression with cyclophosphamide (Cy) as a comparative treatment. METHODS: In this retrospective monocentric 1:2 matched study, SP-MS patients were treated with intermediate-intensity AHSCT (cases) or intravenous pulses of Cy (controls) at a single academic centre in Florence. Controls were selected according to baseline characteristics adopting cardinality matching after trimming on the estimated propensity score. Kaplan-Meier and Cox analyses were used to estimate survival free from relapses (R-FS), survival free from disability progression (P-FS), and no evidence of disease activity 2 (NEDA-2). RESULTS: A total of 93 SP-MS patients were included: 31 AHSCT, 62 Cy. Mean follow-up was 99 months in the AHSCT group and 91 months in the Cy group. R-FS was higher in AHSCT compared to Cy patients: at Year 5, 100% versus 52%, respectively (p < 0.0001). P-FS did not differ between the groups (at Year 5: 70% in AHSCT and 81% in Cy, p = 0.572), nor did NEDA-2 (p = 0.379). A sensitivity analysis including only the 31 "best-matched" controls confirmed these results. Three neoplasms (2 Cy, 1 AHSCT) and two fatalities (2 Cy) occurred. CONCLUSIONS: This study provides Class III evidence, in SP-MS, on the superior effectiveness of AHSCT compared to Cy on relapse activity, without differences on disability accrual. Although the suppression of relapses was observed in the AHSCT group only, AHSCT did not show advantages over Cy on disability, suggesting that in SP-MS disability progression becomes based more on noninflammatory neurodegeneration than on inflammation.

Implications of the Conformationally Flexible, Macrocyclic Structure of the First-Generation, Direct-Acting Anti-Viral Paritaprevir on Its Solid Form Complexity and Chameleonic Behavior.

Direct-acting antiviral regimens have transformed therapeutic management of hepatitis C across all prevalent genotypes. Most of the chemical matter in these regimens comprises molecules well outside the traditional drug development chemical space and presents significant challenges. Herein, the implications of high conformational flexibility and the presence of a 15-membered macrocyclic ring in paritaprevir are studied through a combination of advanced computational and experimental methods with focus on molecular chameleonicity and crystal form complexity. The ability of the molecule to toggle between high and low 3D polar surface area (PSA) conformations is underpinned by intramolecular hydrogen bonding (IMHB) interactions and intramolecular steric effects. Computational studies consequently show a very significant difference of over 75 Å2 in 3D PSA between polar and apolar environments and provide the structural basis for the perplexingly favorable passive permeability of the molecule. Crystal packing and protein binding resulting in strong intermolecular interactions disrupt these intramolecular interactions. Crystalline Form I benefits from strong intermolecular interactions, whereas the weaker intermolecular interactions in Form II are partially compensated by the energetic advantage of an IMHB. Like Form I, no IMHB is observed within the receptor-bound conformation; instead, an intermolecular H-bond contributes to the potency of the molecule. The choice of metastable Form II is derisked through strategies accounting for crystal surface and packing features to manage higher form specific solid-state chemical reactivity and specific processing requirements. Overall, the results show an unambiguous link between structural features and derived properties from crystallization to dissolution, permeation, and docking into the protein pocket.

Novel Physics-Based Ensemble Modeling Approach That Utilizes 3D Molecular Conformation and Packing to Access Aqueous Thermodynamic Solubility: A Case Study of Orally Available Bromodomain and Extraterminal Domain Inhibitor Lead Optimization Series.

Drug design with patient centricity for ease of administration and pill burden requires robust understanding of the impact of chemical modifications on relevant physicochemical properties early in lead optimization. To this end, we have developed a physics-based ensemble approach to predict aqueous thermodynamic crystalline solubility, with a 2D chemical structure as the input. Predictions for the bromodomain and extraterminal domain (BET) inhibitor series show very close match (0.5 log unit) with measured thermodynamic solubility for cases with low crystal anisotropy and good match (1 log unit) for high anisotropy structures. The importance of thermodynamic solubility is clearly demonstrated by up to a 4 log unit drop in solubility compared to kinetic (amorphous) solubility in some cases and implications thereof, for instance on human dose. We have also demonstrated that incorporating predicted crystal structures in thermodynamic solubility prediction is necessary to differentiate (up to 4 log unit) between solubility of molecules within the series. Finally, our physics-based ensemble approach provides valuable structural insights into the origins of 3-D conformational landscapes, crystal polymorphism, and anisotropy that can be leveraged for both drug design and development.

Evaluating a SARS-CoV-2 screening strategy based on serological tests.

BACKGROUND: facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. Although RT-PCR is the most reliable technique to detect ongoing infections, serological tests are frequently proposed as tools in heterogeneous screening strategies. OBJECTIVES: to analyse the performance of a screening strategy proposed by the local government of Tuscany (Central Italy), which first uses qualitative rapid tests for antibody detection, and then RT-PCR tests on the positive subjects. METHODS: a simulation study is conducted to investigate the number of RT-PCR tests required by the screening strategy and the undetected ongoing infections in a pseudo-population of 500,000 subjects, under different prevalence scenarios and assuming a sensitivity of the serological test ranging from 0.50 to 0.80 (specificity 0.98). A compartmental model is used to predict the number of new infections generated by the false negatives two months after the screening, under different values of the infection reproduction number. RESULTS: assuming a sensitivity equal to 0.80 and a prevalence of 0.3%, the screening procedure would require on average 11,167 RT-PCR tests and would produce 300 false negatives, responsible after two months of a number of contagions ranging from 526 to 1,132, under the optimistic scenario of a reproduction number between 0.5 to 1. Resources and false negatives increase with the prevalence. CONCLUSIONS: the analysed screening procedure should be avoided unless the prevalence and the rate of contagion are very low. The cost and effectiveness of the screening strategies should be evaluated in the actual context of the epidemic, accounting for the fact that it may change over time.

Bayesian inference for causal mechanisms with application to a randomized study for postoperative pain control.

We conduct principal stratification and mediation analysis to investigate to what extent the positive overall effect of treatment on postoperative pain control is mediated by postoperative self administration of intra-venous analgesia by patients in a prospective, randomized, double-blind study. Using the Bayesian approach for inference, we estimate both associative and dissociative principal strata effects arising in principal stratification, as well as natural effects from mediation analysis. We highlight that principal stratification and mediation analysis focus on different causal estimands, answer different causal questions, and involve different sets of structural assumptions.

Assessing the short term impact of air pollution on mortality: a matching approach.

BACKGROUND: The opportunity to assess short term impact of air pollution relies on the causal interpretation of the exposure-response association. However, up to now few studies explicitly faced this issue within a causal inference framework. In this paper, we reformulated the problem of assessing the short term impact of air pollution on health using the potential outcome approach to causal inference. We considered the impact of high daily levels of particulate matter ≤10 µm in diameter (PM10) on mortality within two days from the exposure in the metropolitan area of Milan (Italy), during the period 2003-2006. Our research focus was the causal impact of a hypothetical intervention setting daily air pollution levels under a pre-fixed threshold. METHODS: We applied a matching procedure based on propensity score to estimate the total number of attributable deaths (AD) during the study period. After defining the number of attributable deaths in terms of difference between potential outcomes, we used the estimated propensity score to match each high exposure day, namely each day with a level of exposure higher than 40 µg/m3, with a day with similar background characteristics but a level of exposure lower than 40 µg/m3. Then, we estimated the impact by comparing mortality between matched days. RESULTS: During the study period daily exposures larger than 40 µg/m3 were responsible for 1079 deaths (90% CI: 116; 2042). The impact was more evident among the elderly than in the younger age classes. Exposures ≥ 40 µg/m3 were responsible, among the elderly, for 1102 deaths (90% CI: 388, 1816), of which 797 from cardiovascular causes and 243 from respiratory causes. Clear evidence of an impact on respiratory mortality was found also in the age class 65-74, with 87 AD (90% CI: 11, 163). CONCLUSIONS: The propensity score matching turned out to be an appealing method to assess historical impacts in this field, which guarantees that the estimated total number of AD can be derived directly as sum of either age-specific or cause-specific AD, unlike the standard model-based procedure. For this reason, it is a promising approach to perform surveillance focusing on very specific causes of death or diseases, or on susceptible subpopulations. Finally, the propensity score matching is free from issues concerning the exposure-confounders-mortality modeling and does not involve extrapolation. On the one hand this enhances the internal validity of our results; on the other, it makes the approach scarcely appropriate for estimating future impacts.

Identification of causal effects in the presence of nonignorable missing outcome values.

We consider a new approach to identify the causal effects of a binary treatment when the outcome is missing on a subset of units and dependence of nonresponse on the outcome cannot be ruled out even after conditioning on observed covariates. We provide sufficient conditions under which the availability of a binary instrument for nonresponse allows us to derive tighter identification intervals for causal effects in the whole population and to partially identify causal effects in some latent subgroups of units, named Principal Strata, defined by the nonresponse behavior in all possible combinations of treatment and instrument. A simulation study is used to assess the benefits of the presence versus the absence of an instrument for nonresponse. The simulation design is based on real health data, coming from a randomized trial on breast self-examination (BSE) affected by a large proportion of missing outcome data. An instrument for nonresponse is simulated considering alternative scenarios to discuss the key role of the instrument for nonresponse in identifying average causal effects in presence of nonignorable missing outcomes. We also investigate the potential inferential gains from using an instrument for nonresponse adopting a Bayesian approach for inference. In virtue of our theoretical and empirical results, we provide some recommendations on study designs for causal inference.

Comparative efficacy of subcutaneous versus intravenous natalizumab on annualized relapse rate: A post-hoc analysis of the REFINE study.

BACKGROUND: The non-inferiority of the efficacy of subcutaneous (SC) vs intravenous (IV) administration of natalizumab (NTZ) once every 4 weeks in relapsing-remitting multiple sclerosis (RRMS) was recently demonstrated on the primary outcome of the REFINE study, i.e. MRI "combined unique active lesions number" (CUAL). To provide further evidence on the comparative efficacy of the two NTZ formulations, the effect of NTZ-SC vs NTZ-IV on annualized relapse rate (ARR) was investigated re-analysing the REFINE dataset. METHODS: Post-hoc analysis of the REFINE study dataset aimed at exploring the non-inferiority of the efficacy of NTZ-SC vs NTZ-IV on ARR, i.e. the main secondary outcome of the REFINE study. Robustness of the non-inferiority analysis on CUAL with respect to the presence of cases from the SC arm who received a rescue treatment, including NTZ-IV, was also assessed by sensitivity analyses. Three non-inferiority margins were selected, corresponding to 25 %, 33 %, and 50 % fractions of the effect size of NTZ-IV vs placebo observed in the AFFIRM study on ARR (i.e. 0.125, 0.170, and 0.250). RESULTS: Ninety-nine RRMS patients were included. The mean difference in the effect of NTZ-SC vs NTZ-IV on ARR was close to 0. The lower bound of the 95 % confidence interval (worst case scenario) was -0.119, corresponding to 25 % (p = 0.025) of the effect of NTZ-IV vs placebo on ARR. Sensitivity analyses confirmed the results of the primary non-inferiority analysis on the outcome CUAL. CONCLUSIONS: NTZ-SC resulted not inferior to NTZ-IV on ARR for all the non-inferiority margins. The non-inferiority analysis of the efficacy of NTZ-SC vs NTZ-IV on CUAL was demonstrated to be robust with respect to rescued patients.

Seasonal variation, weather and behavior in day-care children: a multilevel approach.

This study analyzes the effect of weather variables, such as solar radiation, indoor and outdoor air temperature, relative humidity and time spent outdoor, on the behavior of 2-year-old children and their affects across different seasons: winter, spring and summer. Participants were a group of 61 children (33 males and 28 females) attending four day-care centers in Florence (Central Italy). Mean age of children at the beginning of the study was 24.1 months (SD = 3.6). We used multilevel linear analyses to account for the hierarchical structure of our data. The study analyzed the following behavioral variables: Activity Level, Attentional Focusing, Frustration, and Aggression. Results showed a different impact of some weather variables on children's behavior across seasons, indicating that the weather variable that affects children's behavior is usually the one that shows extreme values during the studied seasons, such as air temperature and relative humidity in winter and summer. Studying children and their reactions to weather conditions could have potentially wide-reaching implications for parenting and teaching practices, as well as for researchers studying social relationships development.

Free Energy Perturbation Approach for Accurate Crystalline Aqueous Solubility Predictions.

Early assessment of crystalline thermodynamic solubility continues to be elusive for drug discovery and development despite its critical importance, especially for the ever-increasing fraction of poorly soluble drug candidates. Here we present a detailed evaluation of a physics-based free energy perturbation (FEP+) approach for computing the thermodynamic aqueous solubility. The predictive power of this approach is assessed across diverse chemical spaces, spanning pharmaceutically relevant literature compounds and more complex AbbVie compounds. Our approach achieves predictive (RMSE = 0.86) and differentiating power (R2 = 0.69) and therefore provides notably improved correlations to experimental solubility compared to state-of-the-art machine learning approaches that utilize quantum mechanics-based descriptors. The importance of explicit considerations of crystalline packing in predicting solubility by the FEP+ approach is also highlighted in this study. Finally, we show how computed energetics, including hydration and sublimation free energies, can provide further insights into molecule design to feed the medicinal chemistry DMTA cycle.

Polymorph formation and nucleation mechanism of tolfenamic acid in solution: an investigation of pre-nucleation solute association.

PURPOSE: Crystallization from solution involves nucleation and growth; growth conditions greatly influence self-association behaviors of solute molecules in these steps, affecting crystal packing of organic molecules. We examined the role of pre-nucleation association to provide insights into the mutual influence between molecular conformation in solution and packing in the solid state. METHODS: Crystallization experiments of tolfenamic acid were conducted in ethanol under different supersaturation conditions. UV spectroscopy was performed to study self-association of solute molecules in ethanol as a function of concentration. Intermolecular interaction energies of tolfenamic acid dimers were calculated with quantum mechanical methods. RESULTS: As supersaturation increased, growth of the most stable polymorph outpaced the metastable one, contradicting Ostwald's Rule of Stages. UV spectroscopy measurement suggests solute molecules exist as hydrogen-bonded dimers and more dimers form as total concentration increases. Hydrogen bonding in the most stable form is significantly stronger than that in the metastable form. CONCLUSIONS: With the fact that molecular conformation is different in the two polymorphs, as concentration increases, solute molecules rearrange their conformations to form stronger hydrogen-bonded dimers in solution, resulting in nucleation of the most stable form.

Efficient Crystal Structure Prediction for Structurally Related Molecules with Accurate and Transferable Tailor-Made Force Fields.

Crystal structure prediction (CSP) is generally used to complement experimental solid form screening and applied to individual molecules in drug development. The fast development of algorithms and computing resources offers the opportunity to use CSP earlier and for a broader range of applications in the drug design cycle. This study presents a novel paradigm of CSP specifically designed for structurally related molecules, referred to as Quick-CSP. The approach prioritizes more accurate physics through robust and transferable tailor-made force fields (TMFFs), such that significant efficiency gains are achieved through the reduction of expensive ab initio calculations. The accuracy of the TMFF is increased by the introduction of electrostatic multipoles, and the fragment-based force field parameterization scheme is demonstrated to be transferable for a family of chemically related molecules. The protocol is benchmarked with structurally related compounds from the Bromodomain and Extraterminal (BET) domain inhibitors series. A new convergence criterion is introduced that aims at performing only as many ab initio optimizations of crystal structures as required to locate the bottom of the crystal energy landscape within a user-defined accuracy. The overall approach provides significant cost savings ranging from three- to eight-fold less than the full-CSP workflow. The reported advancements expand the scope and utility of the underlying CSP building blocks as well as their novel reassembly to other applications earlier in the drug design cycle to guide molecule design and selection.

Weather daily variation in winter and its effect on behavior and affective states in day-care children.

This study aimed to analyze the impact of winter weather conditions on young children's behavior and affective states by examining a group of 61 children attending day-care centers in Florence (Italy). Participants were 33 males, 28 females and their 11 teachers. The mean age of the children at the beginning of the observation period was 24.1 months. The day-care teachers observed the children's behavioral and emotional states during the morning before their sleeping time and filled in a questionnaire for each baby five times over a winter period of 3 weeks. Air temperature, relative humidity, air pressure and solar radiation data were collected every 15 min from a weather station located in the city center of Florence. At the same time, air temperature and relative humidity data were collected in the classroom and in the garden of each day-care center. We used multilevel linear models to evaluate the extent to which children's emotional and behavioral states could be predicted by weather conditions, controlling for child characteristics (gender and age). The data showed that relative humidity and solar radiation were the main predictors of the children's emotional and behavioral states. The outdoor humidity had a significant positive effect on frustration, sadness and aggression; solar radiation had a significant negative effect only on sadness, suggesting that a sunny winter day makes children more cheerful. The results are discussed in term of implications for parents and teachers to improve children's ecological environment.

Pool testing on random and natural clusters of individuals: Optimisation of SARS-CoV-2 surveillance in the presence of low viral load samples.

Facing the SARS-CoV-2 epidemic requires intensive testing on the population to early identify and isolate infected subjects. During the first emergency phase of the epidemic, RT-qPCR on nasopharyngeal (NP) swabs, which is the most reliable technique to detect ongoing infections, exhibited limitations due to availability of reagents and budget constraints. This stressed the need to develop screening procedures that require fewer resources and are suitable to be extended to larger portions of the population. RT-qPCR on pooled samples from individual NP swabs seems to be a promising technique to improve surveillance. We performed preliminary experimental analyses aimed to investigate the performance of pool testing on samples with low viral load and we evaluated through Monte Carlo (MC) simulations alternative screening protocols based on sample pooling, tailored to contexts characterized by different infection prevalence. We focused on the role of pool size and the opportunity to develop strategies that take advantage of natural clustering structures in the population, e.g. families, school classes, hospital rooms. Despite the use of a limited number of specimens, our results suggest that, while high viral load samples seem to be detectable even in a pool with 29 negative samples, positive specimens with low viral load may be masked by the negative samples, unless smaller pools are used. The results of MC simulations confirm that pool testing is useful in contexts where the infection prevalence is low. The gain of pool testing in saving resources can be very high, and can be optimized by selecting appropriate group sizes. Exploiting natural groups makes the definition of larger pools convenient and potentially overcomes the issue of low viral load samples by increasing the probability of identifying more than one positive in the same pool.

The impact of job uncertainty on first-birth postponement.

This paper aims to advance our understanding of entry into employment with uncertain conditions in Italy and its causal impact on the onset of the fertility process. We adopt the potential outcome approach to causal inference so as to quantify the net effect of having a first job with a temporary or a permanent contract on the propensity to have a first child within the first five years of employment. The analysis is based on retrospective data from the nationally representative 2009 Family and Social Subjects survey. Our results suggest that 7% of potential first-birth postponement among women and 5% of potential postponement among men is attributable to jobs with uncertain conditions. These individuals would have had a first child if they had had a permanent job. For women, potential postponement is elevated among those with higher education (reaching 16%), while for men potential postponement is especially visible among those with low and medium education. With this paper we quantify a non-negligible negative effect for early exposure to labour market uncertainties on potential first-birth postponement in Italy.