Development of allosteric and selective CDK2 inhibitors for contraception with negative cooperativity to cyclin binding.

Compared to most ATP-site kinase inhibitors, small molecules that target an allosteric pocket have the potential for improved selectivity due to the often observed lower structural similarity at these distal sites. Despite their promise, relatively few examples of structurally confirmed, high-affinity allosteric kinase inhibitors exist. Cyclin-dependent kinase 2 (CDK2) is a target for many therapeutic indications, including non-hormonal contraception. However, an inhibitor against this kinase with exquisite selectivity has not reached the market because of the structural similarity between CDKs. In this paper, we describe the development and mechanism of action of type III inhibitors that bind CDK2 with nanomolar affinity. Notably, these anthranilic acid inhibitors exhibit a strong negative cooperative relationship with cyclin binding, which remains an underexplored mechanism for CDK2 inhibition. Furthermore, the binding profile of these compounds in both biophysical and cellular assays demonstrate the promise of this series for further development into a therapeutic selective for CDK2 over highly similar kinases like CDK1. The potential of these inhibitors as contraceptive agents is seen by incubation with spermatocyte chromosome spreads from mouse testicular explants, where they recapitulate Cdk2-/- and Spdya-/- phenotypes.

Epidemiology of Connectional Silence in specialist serious illness conversations.

CONTEXT: Human connection can reduce suffering and facilitate meaningful decision-making amid the often terrifying experience of hospitalization for advanced cancer. Some conversational pauses indicate human connection, but we know little about their prevalence, distribution or association with outcomes. PURPOSE: To describe the epidemiology of Connectional Silence during serious illness conversations in advanced cancer. METHODS: We audio-recorded 226 inpatient palliative care consultations at two academic centers. We identified pauses lasting 2+ seconds and distinguished Connectional Silences from other pauses, sub-categorized as either Invitational (ICS) or Emotional (ECS). We identified treatment decisional status pre-consultation from medical records and post-consultation via clinicians. Patients self-reported quality-of-life before and one day after consultation. RESULTS: Among all 6769 two-second silences, we observed 328 (4.8%) ECS and 240 (3.5%) ICS. ECS prevalence was associated with decisions favoring fewer disease-focused treatments (ORadj: 2.12; 95% CI: 1.12, 4.06). Earlier conversational ECS was associated with improved quality-of-life (p = 0.01). ICS prevalence was associated with clinicians' prognosis expectations. CONCLUSIONS: Connectional Silences during specialist serious illness conversations are associated with decision-making and improved patient quality-of-life. Further work is necessary to evaluate potential causal relationships. PRACTICE IMPLICATIONS: Pauses offer important opportunities to advance the science of human connection in serious illness decision-making.

Epidemiology of Fear, Sadness, and Anger Expression in Palliative Care Conversations.

CONTEXT: Advancing the science of serious illness communication requires methods for measuring characteristics of conversations in large studies. Understanding which characteristics predict clinically important outcomes can help prioritize attention to scalable measure development. OBJECTIVES: To understand whether audibly recognizable expressions of distressing emotion during palliative care serious illness conversations are associated with ratings of patient experience or six-month enrollment in hospice. METHODS: We audiorecorded initial palliative care consultations involving 231 hospitalized people with advanced cancer at two large academic medical centers. We coded conversations for expressions of fear, anger, and sadness. We examined the distribution of these expressions and their association with pre/post ratings of feeling heard and understood and six-month hospice enrollment after the consultation. RESULTS: Nearly six in 10 conversations included at least one audible expression of distressing emotion (59%; 137 of 231). Among conversations with such an expression, fear was the most prevalent (72%; 98 of 137) followed by sadness (50%; 69 of 137) and anger (45%; 62 of 137). Anger expression was associated with more disease-focused end-of-life treatment preferences, pre/post consultation improvement in feeling heard and understood and lower six-month hospice enrollment. Fear was strongly associated with preconsultation patient ratings of shorter survival expectations. Sadness did not exhibit strong association with patient descriptors or outcomes. CONCLUSION: Fear, anger, and sadness are commonly expressed in hospital-based palliative care consultations with people who have advanced cancer. Anger is an epidemiologically useful predictor of important clinical outcomes.

Story Arcs in Serious Illness: Natural Language Processing features of Palliative Care Conversations.

OBJECTIVE: Serious illness conversations are complex clinical narratives that remain poorly understood. Natural Language Processing (NLP) offers new approaches for identifying hidden patterns within the lexicon of stories that may reveal insights about the taxonomy of serious illness conversations. METHODS: We analyzed verbatim transcripts from 354 consultations involving 231 patients and 45 palliative care clinicians from the Palliative Care Communication Research Initiative. We stratified each conversation into deciles of "narrative time" based on word counts. We used standard NLP analyses to examine the frequency and distribution of words and phrases indicating temporal reference, illness terminology, sentiment and modal verbs (indicating possibility/desirability). RESULTS: Temporal references shifted steadily from talking about the past to talking about the future over deciles of narrative time. Conversations progressed incrementally from "sadder" to "happier" lexicon; reduction in illness terminology accounted substantially for this pattern. We observed the following sequence in peak frequency over narrative time: symptom terms, treatment terms, prognosis terms and modal verbs indicating possibility. CONCLUSIONS: NLP methods can identify narrative arcs in serious illness conversations. PRACTICE IMPLICATIONS: Fully automating NLP methods will allow for efficient, large scale and real time measurement of serious illness conversations for research, education and system re-design.