Annual review of pharmacology and toxicology最新文献

Adverse nocebo responses can cause harm to patients and interfere with treatment adherence and effects in both clinic practice and clinical trials. Nocebo responses refer to negative outcomes to active medical treatments in clinical trials or practice that cannot be explained by the treatment's pharmacologic effects. Negative expectancies and nocebo effects are less known than placebo responses. Nocebo effects can be triggered by verbal suggestions, prior negative experiences, observation of others experiencing negative outcomes, and other contextual and environmental factors. As research advances over the years, mechanistic knowledge is accumulating on the neurobiological mechanisms of nocebo effects. This review summarizes studies on different facets of nocebo effects and responses and discusses clinical implications, ethical considerations, and future directions.

Pharmacogenomics (PGx) enables personalized treatment for the prediction of drug response and to avoid adverse drug reactions. Currently, PGx mainly relies on the genetic information of absorption, distribution, metabolism, and excretion (ADME) targets such as drug-metabolizing enzymes or transporters to predict differences in the patient's phenotype. However, there is evidence that the phenotype-genotype concordance is limited. Thus, we discuss different phenotyping strategies using exogenous xenobiotics (e.g., drug cocktails) or endogenous compounds for phenotype prediction. In particular, minimally invasive approaches focusing on liquid biopsies offer great potential to preemptively determine metabolic and transport capacities. Early studies indicate that ADME phenotyping using exosomes released from the liver is reliable. In addition, pharmacometric modeling and artificial intelligence improve phenotype prediction. However, further prospective studies are needed to demonstrate the clinical utility of individualized treatment based on phenotyping strategies, not only relying on genetics. The present review summarizes current knowledge and limitations.

Proteogenomics refers to the integration of comprehensive genomic, transcriptomic, and proteomic measurements from the same samples with the goal of fully understanding the regulatory processes converting genotypes to phenotypes, often with an emphasis on gaining a deeper understanding of disease processes. Although specific genetic mutations have long been known to drive the development of multiple cancers, gene mutations alone do not always predict prognosis or response to targeted therapy. The benefit of proteogenomics research is that information obtained from proteins and their corresponding pathways provides insight into therapeutic targets that can complement genomic information by providing an additional dimension regarding the underlying mechanisms and pathophysiology of tumors. This review describes the novel insights into tumor biology and drug resistance derived from proteogenomic analysis while highlighting the clinical potential of proteogenomic observations and advances in technique and analysis tools.

Apathy is a disabling syndrome associated with poor functional outcomes that is common across a broad range of neurological and psychiatric conditions. Currently, there are no established therapies specifically for the condition, and safe and effective treatments are urgently needed. Advances in the understanding of motivation and goal-directed behavior in humans and animals have shed light on the cognitive and neurobiological mechanisms contributing to apathy, providing an important foundation for the development of new treatments. Here, we review the cognitive components, neural circuitry, and pharmacology of apathy and motivation, highlighting converging evidence of shared transdiagnostic mechanisms. Though no pharmacological treatments have yet been licensed, we summarize trials of existing and novel compounds to date, identifying several promising candidates for clinical use and avenues of future drug development.

Thalidomide and its derivatives are powerful cancer therapeutics that are among the best-understood molecular glue degraders (MGDs). These drugs selectively reprogram the E3 ubiquitin ligase cereblon (CRBN) to commit target proteins for degradation by the ubiquitin-proteasome system. MGDs create novel recognition interfaces on the surface of the E3 ligase that engage in induced protein-protein interactions with neosubstrates. Molecular insight into their mechanism of action opens exciting opportunities to engage a plethora of targets through a specific recognition motif, the G-loop. Our analysis shows that current CRBN-based MGDs can in principle recognize over 2,500 proteins in the human proteome that contain a G-loop. We review recent advances in tuning the specificity between CRBN and its MGD-induced neosubstrates and deduce a set of simple rules that govern these interactions. We conclude that rational MGD design efforts will enable selective degradation of many more proteins, expanding this therapeutic modality to more disease areas.

Today's challenge for precision medicine involves the integration of the impact of molecular clocks on drug pharmacokinetics, toxicity, and efficacy toward personalized chronotherapy. Meaningful improvements of tolerability and/or efficacy of medications through proper administration timing have been confirmed over the past decade for immunotherapy and chemotherapy against cancer, as well as for commonly used pharmacological agents in cardiovascular, metabolic, inflammatory, and neurological conditions. Experimental and human studies have recently revealed sexually dimorphic circadian drug responses. Dedicated randomized clinical trials should now aim to issue personalized circadian timing recommendations for daily medical practice, integrating innovative technologies for remote longitudinal monitoring of circadian metrics, statistical prediction of molecular clock function from single-timepoint biopsies, and multiscale biorhythmic mathematical modelling. Importantly, chronofit patients with a robust circadian function, who would benefit most from personalized chronotherapy, need to be identified. Conversely, nonchronofit patients could benefit from the emerging pharmacological class of chronobiotics targeting the circadian clock.

Direct oral anticoagulants (DOACs) have largely replaced vitamin K antagonists, mostly warfarin, for the main indications for oral anticoagulation, prevention and treatment of venous thromboembolism, and prevention of embolic stroke in atrial fibrillation. While DOACs offer practical, fixed-dose anticoagulation in many patients, specific restrictions or contraindications may apply. DOACs are not sufficiently effective in high-thrombotic risk conditions such as antiphospholipid syndrome and mechanical heart valves. Patients with cancer-associated thrombosis may benefit from DOACs, but the bleeding risk, particularly in those with gastrointestinal or urogenital tumors, must be carefully weighed. In patients with frailty, excess body weight, and/or moderate-to-severe chronic kidney disease, DOACs must be cautiously administered and may require laboratory monitoring. Reversal agents have been developed and approved for life-threatening bleeding. In addition, the clinical testing of potentially safer anticoagulants such as factor XI(a) inhibitors is important to further optimize anticoagulant therapy in an increasingly elderly and frail population worldwide.

Drug discovery is adapting to novel technologies such as data science, informatics, and artificial intelligence (AI) to accelerate effective treatment development while reducing costs and animal experiments. AI is transforming drug discovery, as indicated by increasing interest from investors, industrial and academic scientists, and legislators. Successful drug discovery requires optimizing properties related to pharmacodynamics, pharmacokinetics, and clinical outcomes. This review discusses the use of AI in the three pillars of drug discovery: diseases, targets, and therapeutic modalities, with a focus on small-molecule drugs. AI technologies, such as generative chemistry, machine learning, and multiproperty optimization, have enabled several compounds to enter clinical trials. The scientific community must carefully vet known information to address the reproducibility crisis. The full potential of AI in drug discovery can only be realized with sufficient ground truth and appropriate human intervention at later pipeline stages.

Each year over half a million people experience permanent hearing loss caused by treatment with therapeutic drugs with ototoxic side effects. There is a major unmet clinical need for therapies that protect against this hearing loss without reducing the therapeutic efficacy of these lifesaving drugs. At least 17 clinical trials evaluating 10 therapeutics are currently underway for therapies aimed at preventing aminoglycoside- and/or cisplatin-induced ototoxicity. This review describes the preclinical and clinical development of each of these approaches, provides updates on the status of ongoing trials, and highlights the importance of appropriate outcome measures in trial design and the value of reporting criteria in the dissemination of results.

Lipoprotein(a) [Lp(a)] is a molecule bound to apolipoprotein(a) with some similarity to low-density lipoprotein cholesterol (LDL-C), which has been found to be a risk factor for cardiovascular disease (CVD). Lp(a) appears to induce inflammation, atherogenesis, and thrombosis. Approximately 20% of the world's population has increased Lp(a) levels, determined predominantly by genetics. Current clinical practices for the management of dyslipidemia are ineffective in lowering Lp(a) levels. Evolving RNA-based therapeutics, such as the antisense oligonucleotide pelacarsen and small interfering RNA olpasiran, have shown promising results in reducing Lp(a) levels. Phase III pivotal cardiovascular outcome trials [Lp(a)HORIZON and OCEAN(a)] are ongoing to evaluate their efficacy in secondary prevention of major cardiovascular events in patients with elevated Lp(a). The future of cardiovascular residual risk reduction may transition to a personalized approach where further lowering of either LDL-C, triglycerides, or Lp(a) is selected after high-intensity statin therapy based on the individual risk profile and preferences of each patient.