Understanding molecular self-association in solution is vital for uncovering polymorph-selective crystal nucleation pathways. In this paper, we combine solution NMR spectroscopy and molecular dynamics simulations to shed light on the structural and dynamical features of p-aminobenzoic acid (pABA) in solution, and on their role in pABA crystals nucleation. pABA is known to yield different crystal forms (α, and β) depending on solvent choice and supersaturation conditions. NMR reveals that dominant interactions stabilizing pABA oligomers are markedly solvent-dependent: in organic solvents, hydrogen bonds dominate, while water promotes π-πstacking. Despite this clear preference, both types of interactions contribute to the variety of self-associated species in all solvents considered. MD simulations support this observation and show that pABA oligomers are short-lived and display a fluxional character, therefore indicating that the growth unit involved in pABA crystallization is likely to be a single molecule. Nevertheless, we note that the interactions dominating in pABA oligomers are indicative of the polymorph obtained from precipitation. In water, at low pABA concentrations - conditions that are known to yield crystals of the β form - carboxylic-carboxylic hydrogen bonds are exclusively asymmetric. At higher pABA concentration conditions in which the crystallization is known to yield the α form - a small but statistically significant fraction of symmetric carboxylic-carboxylic hydrogen-bonded dimers is present. We interpret the presence of these interactions in solvated pABA oligomers as indicative of the fact that a simultaneous and complete desolvation of two carboxylic groups, necessary to form the symmetric hydrogen-bonded dimer typical of the α crystal form, is accessible, therefore directing the nucleation pathway toward the nucleation of α-pABA.

Polymorph-Selective Role of Hydrogen Bonding and π-π Stacking in p-Aminobenzoic Acid Solutions

Controlling the physical properties of solid forms for active pharmaceutical ingredients (APIs) through cocrystallization is an important part of drug product development. However, it is difficult to know a priori which coformers will form cocrystals with a given API, and the current state-of-the-art for cocrystal discovery involves an expensive, time-consuming, and, at the early stages of pharmaceutical development, API material-limited experimental screen. We propose a systematic, high-throughput computational approach primarily aimed at identifying API/coformer pairs that are unlikely to lead to experimentally observable cocrystals and can therefore be eliminated with only a brief experimental check, from any experimental investigation. On the basis of a well-established crystal structure prediction (CSP) methodology, the proposed approach derives its efficiency by not requiring any expensive quantum mechanical calculations beyond those already performed for the CSP investigation of the neat API itself. The approach and assumptions are tested through a computational investigation on 30 potential 1:1 multicomponent systems (cocrystals and solvate) involving 3 active pharmaceutical ingredients and 9 coformers and one solvent. This is complemented with a detailed experimental investigation of all 30 pairs, which led to the discovery of five new cocrystals (three API–coformer combinations, a polymorphic cocrystal example, and one with different stoichiometries) and a cis-aconitic acid polymorph. The computational approach indicates that, for some APIs, a significant proportion of all potential API/coformer pairs could be investigated with only a brief experimental check, thereby saving considerable experimental effort.

https://pubs.acs.org/doi/pdf/10.1021/acs.cgd.2c00433

Efficient Screening of Coformers for Active Pharmaceutical Ingredient Cocrystallization

Solvent–Antisolvent Competitive Interactions Mediate Imidacloprid Polymorphs in Antisolvent Crystallization

The discovery of new crystalline forms of large, flexible drug molecules is of significant interest to the pharmaceutical industry as a means of improving one or more physicochemical properties of the active pharmaceutical ingredient (API). Trametinib (TRMB) is an anticancer drug, for which no solid-state forms are yet reported. Here, we report the results of an extensive screen for cocrystals of TRMB in an effort to improve the poor aqueous solubility of this API. Contrary to expectations, this effort led to the discovery of only six isostructural solvates of TRMB, where the solvent molecules fill the voids of the crystal structure and are stabilized by various intermolecular interactions. Computational crystal structure prediction methods were used to rationalize the high propensity for solvent inclusion in TRMB as arising from the poor crystal packing in TRMB. All predicted polymorphs of TRMB within a relative lattice energy of 44 kJ mol–1 of the global minimum display solvent accessible voids ranging from 1.4 to 16.5% of the unit cell volume. The serendipitous discovery of solvates in experiments targeting cocrystals,strongly suggests that the well-established synthon approach for the discovery of cocrystals works relatively well for documented literature examples that use rigid coformers with minimal conformational flexibility. However, for large APIs such as TRMB, in-silico screening methods should be adopted to reduce the need for extensive experimental cocrystal screens as the ability to form a cocrystal for drug candidates relies on more than synthon complementarity. 

Unravelling the Origin of Solvate Formation in the Anticancer Drug Trametinib: Insights from Crystal Structure Analysis and Computational Modeling

Stronger drug–solvent interactions than drug–drug interactions are responsible for the formation of solvates in niclosamide.

Why does the niclosamide drug form solvates or hydrates?

Classical nucleation theory teaches the idea that molecular clusters form and grow in solution and that depending on prevailing conditions there is a chance for some to grow large enough to overcome the interfacial energy penalty and become mature crystals. However, from such a kinetic analysis, nothing is learnt of the nature of the composition or the molecular packing in such clusters. As a means of addressing this shortcoming consideration has, in the past, been given to the idea that in certain systems crystallography may offer additional, structural, insights. From this approach the notions of ‘nucleation pathway’ or ‘nucleation transition state’ have become useful concepts around which to formulate hypotheses as to how clusters may yield specific molecular packing, resulting for example, in the observation of crystal polymorphs. Here we offer an in-depth crystallographic analysis related to the nucleation of the α and β polymorphs of para-aminobenzoic acid in an attempt to reveal the pathways leading to the two forms. Using a combination of CSD analyses, crystal structure prediction and targeted crystallizations we explore plausible solution pathways to these polymorphs and discuss our results in the light of known kinetic data for the nucleation and growth of this material.

/pdf/can-solvated-intermediates-inform-us-about-nucleation-5es5pbm7kn.pdf

Can solvated intermediates inform us about nucleation pathways? The case of β-pABA

The experiment was conducted to examine the physical properties, pH, temperature, proximate composition, fibre fraction, energy content and mineral composition of ensiled rice milling waste (RMW) treated with urea and poultry litter in a completely randomized design. RMW was treated with of Urea and Poultry Litter (PL) and ensiled in in-vitro laboratory silos (946ml). The treatment combinations were; A (100%RMW), B (100%RMW+ Urea), C (80%RMW + 20%PL), and D (60%RMW + 40%PL). The treatments were ensiled for 21 days in triplicates. At the expiration of each ensiling period, samples were taken for analyses. The results for physical properties showed that most of the silages were adequately fermented with sweet aroma. The compounded silages were acidic 4.34 – 4.86 except treatments B and D which had pH of 7.96 and 7.91 respectively. The temperatures of the silages were significantly (P<0.05) different. Based on the physical properties, pH and temperature evaluated, most of the silages can be considered to be well prepared. The proximate composition of silages differ significantly (P<0.05), higher percentages in CF and Ash were recorded in Treatment A. Dry matter content was also significantly different (P<0.05) with treatment A having the highest DM (97.19%). The CP in treatment D (6.13%) proved superior. RMW and Urea mixtures resulted in silages with highest %NDF, %HEM and Energy. Lowest NDF, ADF, CEL and ADL values were obtained in treatment D. The nutritive quality of treatment D was proved to be higher and thus it is recommended for dry season ruminant feeding.

/pdf/proximate-composition-fibre-fraction-and-mineral-composition-1tl0ixlx.pdf

Proximate composition, fibre fraction and mineral composition of ensiled rice milling waste enhanced with urea and poultry litter

Plant biomass as an alternative energy source serves as a viable option for improving sustainable development and reducing greenhouse gas emissions. However, the tight bonding within its constituents may hinder anaerobic digestion, thus, requires pretreatment to break down the complex polysaccharide structure into simpler disaccharide and monosaccharide sugars to facilitate digestion and enhance the production of biogas. The research determined the effect physical and chemical pre-treatment methods of Typha biomass for biogas production. Characteristics determined include; carbonnitrogen (C-N) ratio for Typha biomass, temperature, pH, total solids (TS) and volatile solids (VS) of slurry formed by mixing the biomass with cow-dung in ratio 1:1. The volume of biogas produced was determined by water displacement method using an anaerobic digester while the mass balance approach was used to estimate the biogas yield from the TS/VS lost. Results indicated 31.6 C-N ratio; pH of 6.7; Temperature of 32.4OC and TS of 11.3%, which falls within suitable ranges reported for biogas production. The volume of biogas produced was 180 cm3, 235cm3 and 118cm3 for control, physical and chemical pre-treated samples respectively. Similarly, the biogas yield was 21mg/l, 15mg/l and 48mg/l for control, physical and chemical pre-treatment respectively. Hence the findings revealed physical pre-treatment as the best pretreatment method for biogas generation from Typha biomass in relation to chemical pretreatment and untreated Typha biomass.

/pdf/effect-of-pretreatment-on-typha-biomass-for-biogas-58qqdfcq.pdf

Effect of pretreatment on Typha biomass for biogas production

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

H. Abdullahi

Author Tools

Chat about Author

Papers

Can solvated intermediates inform us about nucleation pathways? The case of β-pABA

Proximate composition, fibre fraction and mineral composition of ensiled rice milling waste enhanced with urea and poultry litter

Effect of pretreatment on Typha biomass for biogas production