Synthesis of Selective Estrogen Receptor Degrader GDC-0810 via Stereocontrolled Assembly of a Tetrasubstituted All-Carbon Olefin

Utilizing Molecular Simulations to Examine Nanosuspension Stability

Drug nanosuspensions offer a promising approach to improve bioavailability for poorly soluble drug candidates. Such formulations often necessitate the inclusion of an excipient to stabilize the drug nanoparticles. However, the rationale for the choice of the correct excipient for a given drug candidate remains unclear. To gain molecular insight into formulation design, this work first utilizes a molecular dynamics simulation to computationally investigate drug-excipient interactions for a number of combinations that have been previously studied experimentally. We find that hydrophobic interactions drive excipient adsorption to drug nanoparticles and that the fraction of polar surface area serves as a predictor for experimental measurements of nanosuspension stability. To test these ideas prospectively, we applied our model to an uncharacterized drug compound, GDC-0810. Our simulations predicted that a salt form of GDC-0810 would lead to more stable nanosuspensions than the neutral form; therefore, we tested the stability of salt GDC-0810 nanosuspensions and found that the salt form readily formed nanosuspensions even without the excipient. To avoid computationally expensive simulations in the future, we extended our model by showing that simple, two-dimensional properties of single drug molecules can be used to rationalize nanosuspension designs without simulations. In all, our work demonstrates how computational tools can provide molecular insight into drug-excipient interactions and aid in rational formulation design.

A syn outer-sphere oxidative addition: the reaction mechanism in Pd/Senphos-catalyzed carboboration of 1,3-enynes

We report a combined experimental and computational study of Pd/Senphos-catalyzed carboboration of 1,3-enynes utilizing DFT calculations, ³¹P NMR study, kinetic study, Hammett analysis and Arrhenius/Eyring analysis. Our mechanistic study provides evidence against the conventional inner-sphere β-migratory insertion mechanism. Instead, a syn outer-sphere oxidative addition mechanism featuring a Pd-π-allyl intermediate followed by coordination-assisted rearrangements is consistent with all the experimental observations.

Stereoconvergent and -divergent Synthesis of Tetrasubstituted Alkenes by Nickel-Catalyzed Cross-Couplings

We report the development of a method to diastereoselectively access tetrasubstituted alkenes via nickel-catalyzed Suzuki-Miyaura cross-couplings of enol tosylates and boronic acid esters. Either diastereomeric product was selectively accessed from a mixture of enol tosylate starting material diastereomers in a convergent reaction by judicious choice of the ligand and reaction conditions. A similar protocol also enabled a divergent synthesis of each product isomer from diastereomerically pure enol tosylates. Notably, high-throughput optimization of the monophosphine ligands was guided by chemical space analysis of the kraken library to ensure a diverse selection of ligands was examined. Stereoelectronic analysis of the results provided insight into the requirements for reactive and selective ligands in this transformation. The synthetic utility of the optimized catalytic system was then probed in the stereoselective synthesis of various tetrasubstituted alkenes, with yields up to 94% and diastereomeric ratios up to 99:1 Z/E and 93:7 E/Z observed. Moreover, a detailed computational analysis and experimental mechanistic studies provided key insights into the nature of the underlying isomerization process impacting selectivity in the cross-coupling.

GDC-9545 (Giredestrant): A Potent and Orally Bioavailable Selective Estrogen Receptor Antagonist and Degrader with an Exceptional Preclinical Profile for ER+ Breast Cancer

Breast cancer remains a leading cause of cancer death in women, representing a significant unmet medical need. Here, we disclose our discovery efforts culminating in a clinical candidate, 35 (GDC-9545 or giredestrant). 35 is an efficient and potent selective estrogen receptor degrader (SERD) and a full antagonist, which translates into better antiproliferation activity than known SERDs (1, 6, 7, and 9) across multiple cell lines. Fine-tuning the physiochemical properties enabled once daily oral dosing of 35 in preclinical species and humans. 35 exhibits low drug-drug interaction liability and demonstrates excellent in vitro and in vivo safety profiles. At low doses, 35 induces tumor regressions either as a single agent or in combination with a CDK4/6 inhibitor in an ESR1^Y537S mutant PDX or a wild-type ERα tumor model. Currently, 35 is being evaluated in Phase III clinical trials.

Recent advances in the stereoselective synthesis of acyclic all-carbon tetrasubstituted alkenes

Alkenes bearing four carbon-based groups are ubiquitous motifs in chemical sciences due to their various applications from medicinal to materials chemistry, and as chemical platforms for the synthesis of complex, chiral molecules. As such, tremendous research efforts are currently ongoing in order to develop general procedures for the challenging stereoselective synthesis of all-carbon tetrasubstituted alkenes, especially for acyclic structures. Since classical approaches to carbon-carbon double bonds are not suitable for the high steric demand around tetrasubstituted alkenes, a variety of unique approaches to access these privileged functional groups have been developed in recent years. This review article highlights the most significant developments in the field from 2007 to 2020, with an emphasis on the mechanisms and remaining limitations of these contemporary methods. Specifically, recent advances in internal alkyne carbofunctionalizations, in multicomponent couplings or other cross-couplings from nucleophilic or electrophilic alkenyl partners, and in the development of miscellaneous methods, are discussed.

Single-Scan Selective Excitation of Individual NMR Signals in Overlapping Multiplets

2D NMR is an immensely powerful structural tool but it is time-consuming. Targeting individual chemical groups by selective excitation in a 1D experiment can give the information required far more quickly. A major problem, however, is that proton NMR spectra are often extensively overlapped, so that in practice only a minority of sites can be selectively excited. Here we overcome that problem using a fast, single-scan method that allows selective excitation of the signals of a single proton multiplet even where it is severely overlapped by other multiplets. The advantages of the method are illustrated in a selective 1D NOESY experiment, the most efficient way to determine relative configuration unambiguously by NMR. The new approach presented here has the potential to broaden significantly the applicability of selective excitation and unlock its real potential for many other experiments.

Asymmetric Hydrogenation of Unfunctionalized Tetrasubstituted Acyclic Olefins

Asymmetric hydrogenation has evolved as one of the most powerful tools to construct stereocenters. However, the asymmetric hydrogenation of unfunctionalized tetrasubstituted acyclic olefins remains the pinnacle of asymmetric synthesis and an unsolved challenge. We report herein the discovery of an iridium catalyst for the first, generally applicable, highly enantio- and diastereoselective hydrogenation of such olefins and the mechanistic insights of the reaction. The power of this chemistry is demonstrated by the successful hydrogenation of a wide variety of electronically and sterically diverse olefins in excellent yield and high enantio- and diastereoselectivity.

Iron-catalyzed stereospecific arylation of enol tosylates using Grignard reagents

Iron-catalyzed stereospecific arylation of enol tosylates with Grignard reagents.

Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction

Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: Given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between simplified molecular-input line-entry system (SMILES) strings (a text-based representation) of reactants, reagents, and the products. We show that a multihead attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark data set. Molecular Transformer makes predictions by inferring the correlations between the presence and absence of chemical motifs in the reactant, reagent, and product present in the data set. Our model requires no handcrafted rules and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without a reactant-reagent split and including stereochemistry, which makes our method universally applicable.