Drug-target residence time is a crucial determinant of pharmacological efficacy, complementing traditional equilibrium affinity measures. Variations in residence time influence drug selectivity, therapeutic windows, and resistance development, yet its molecular underpinnings remain incompletely understood. Here we review factors governing residence time, including kinetic parameters and structural influences, and examine how mutations can alter dissociation rates to confer drug resistance. We highlight recent advances in experimental and computational methods, such as molecular dynamics simulations, that enable prediction and rational design of compounds with optimized residence times. These insights underscore the importance of incorporating kinetic considerations into drug discovery to improve efficacy and overcome resistance. Our findings suggest that optimizing residence time offers a promising strategy to enhance therapeutic outcomes for diverse diseases.

Allosteric inhibitors have emerged as powerful therapeutic agents capable of overcoming resistance mutations that impair the efficacy of orthosteric inhibitors. However, resistance to allosteric inhibitors can also arise, posing a challenge to their long-term effectiveness. Mechanisms of resistance include altered inhibitor affinity and kinetics, disruption of the allosteric mechanism, changes in receptor recycling and activity, and off-target adaptations such as upregulation of drug efflux pumps or activation of compensatory signaling pathways. Furthermore, the specific mechanism of allosteric regulation induced by inhibitor binding can itself be susceptible to resistance mutations, leading to diminished efficacy. Understanding these diverse resistance mechanisms is crucial for developing strategies to counteract them. One promising approach involves the combination of both allosteric and orthosteric inhibitors, either as separate agents or as linked "bitopic" compounds, to mitigate the impact of resistance mutations. This review explores the molecular basis of resistance to allosteric inhibitors and potential strategies to overcome resistance, offering insights for the development of more resilient therapies.

Small molecule kinase inhibitors are critical in the modern treatment of cancers, evidenced by the existence of over 80 FDA-approved small-molecule kinase inhibitors. Unfortunately, intrinsic or acquired resistance, often causing therapy discontinuation, is frequently caused by mutations in the kinase therapeutic target. The advent of clinical tumor sequencing has opened additional opportunities for precision oncology to improve patient outcomes by pairing optimal therapies with tumor mutation profiles. However, modern precision oncology efforts are hindered by lack of sufficient biochemical or clinical evidence to classify each mutation as resistant or sensitive to existing inhibitors. Structure-based methods show promising accuracy in retrospective benchmarks at predicting whether a kinase mutation will perturb inhibitor binding, but comparisons are made by pooling disparate experimental measurements across different conditions. We present the first prospective benchmark of structure-based approaches on a blinded dataset of in-cell kinase inhibitor affinities to Abl kinase mutants using a NanoBRET reporter assay. We compare NanoBRET results to structure-based methods and their ability to estimate the impact of mutations on inhibitor binding (measured as ΔΔG). Comparing physics-based simulations, Rosetta, and previous machine learning models, we find that structure-based methods accurately classify kinase mutations as inhibitor-resistant or inhibitor-sensitizing, and each approach has a similar degree of accuracy. We show that physics-based simulations are best suited to estimate ΔΔG of mutations that are distal to the kinase active site. To probe modes of failure, we retrospectively investigate two clinically significant mutations poorly predicted by our methods, T315A and L298F, and find that starting configurations and protonation states significantly alter the accuracy of our predictions. Our experimental and computational measurements provide a benchmark for estimating the impact of mutations on inhibitor binding affinity for future methods and structure-based models. These structure-based methods have potential utility in identifying optimal therapies for tumor-specific mutations, predicting resistance mutations in the absence of clinical data, and identifying potential sensitizing mutations to established inhibitors.

Understanding drug residence times in target proteins is key to improving drug efficacy and understanding target recognition in biochemistry. While drug residence time is just as important as binding affinity, atomic-level understanding of drug residence times through molecular dynamics (MD) simulations has been difficult primarily due to the extremely long time scales. Recent advances in rare event sampling have allowed us to reach these time scales, yet predicting protein-ligand residence times remains a significant challenge. Here we present a semi-automated protocol to calculate the ligand residence times across 12 orders of magnitude of time scales. In our proposed framework, we integrate a deep learning-based method, the state predictive information bottleneck (SPIB), to learn an approximate reaction coordinate (RC) and use it to guide the enhanced sampling method metadynamics. We demonstrate the performance of our algorithm by applying it to six different protein-ligand complexes with available benchmark residence times, including the dissociation of the widely studied anticancer drug Imatinib (Gleevec) from both wild-type Abl kinase and drug-resistant mutants. We show how our protocol can recover quantitatively accurate residence times, potentially opening avenues for deeper insights into drug development possibilities and ligand recognition mechanisms.

In this issue of Cell Chemical Biology, Chakraborty et al.1 employ a deep mutational screening analysis of 3,500 single point mutations in every residue in Src kinase’s catalytic domain to determine which residues are critical for conferring ATP-competitive inhibitor resistance. They identify a dynamically controlled resistance site.

Understanding at the molecular level of the cell biology of tumors has led to significant treatment advances in the past. Despite such advances however, development of therapy resistance and tumor recurrence are still unresolved major challenges. This therefore underscores the need to identify novel tumor targets and develop corresponding therapies to supplement existing biologic and cytotoxic approaches so that a deeper and more sustained treatment responses could be achieved. The complement system is emerging as a potential novel target for cancer therapy. Data accumulated to date show that complement proteins, and in particular C1q and its receptors cC1qR/CR and gC1qR/p33/HABP1, are overexpressed in most cancer cells and together are involved not only in shaping the inflammatory tumor microenvironment, but also in the regulation of angiogenesis, metastasis, and cell proliferation. In addition to the soluble form of C1q that is found in plasma, the C1q molecule is also found anchored on the cell membrane of monocytes, macrophages, dendritic cells, and cancer cells, via a 22aa long leader peptide found only in the A-chain. This orientation leaves its 6 globular heads exposed outwardly and thus available for high affinity binding to a wide range of molecular ligands that enhance tumor cell survival, migration, and proliferation. Similarly, the gC1qR molecule is not only overexpressed in most cancer types but is also released into the microenvironment where it has been shown to be associated with cancer cell proliferation and metastasis by activation of the complement and kinin systems. Co-culture of either T cells or cancer cells with purified C1q or anti-gC1qR has been shown to induce an anti-proliferative response. It is therefore postulated that in the tumor microenvironment, the interaction between C1q expressing cancer cells and gC1qR bearing cytotoxic T cells results in T cell suppression in a manner akin to the PD-L1 and PD-1 interaction.

Mutations that decrease or increase the activity of the tyrosine phosphatase, SHP2 (encoded by PTPN11), promotes developmental disorders and several malignancies by varying phosphatase activity. We uncovered that SHP2 is a distinct class of an epigenetic enzyme; upon phosphorylation by the kinase ACK1/TNK2, pSHP2 was escorted by androgen receptor (AR) to chromatin, erasing hitherto unidentified pY54-H3 (phosphorylation of histones H3 at Tyr54) epigenetic marks to trigger a transcriptional program of AR. Noonan Syndrome with Multiple Lentigines (NSML) patients, SHP2 knock-in mice, and ACK1 knockout mice presented dramatic increase in pY54-H3, leading to loss of AR transcriptome. In contrast, prostate tumors with high pSHP2 and pACK1 activity exhibited progressive downregulation of pY54-H3 levels and higher AR expression that correlated with disease severity. Overall, pSHP2/pY54-H3 signaling acts as a sentinel of AR homeostasis, explaining not only growth retardation, genital abnormalities and infertility among NSML patients, but also significant AR upregulation in prostate cancer patients.

Glioblastoma (GBM) is the most frequently diagnosed primary central nervous system tumor in adults. Despite the standard of care therapy, which includes surgical resection, temozolomide chemotherapy, radiation and the newly added tumor-treating fields, median survival remains only ∼20 months. Unfortunately, GBM has a ∼100% recurrence rate, but after recurrence there are no Food and Drug Administration-approved therapies to limit tumor growth and enhance patient survival, as these tumors are resistant to temozolomide (TMZ). Recently, our laboratory reported that lucanthone slows GBM by inhibiting autophagic flux through lysosome targeting and decreases the number of Olig2+ glioma stem-like cells (GSC) in vitro and in vivo. We now additionally report that lucanthone efficiently abates stemness in patient-derived GSC and reduces tumor microtube formation in GSC, an emerging hallmark of treatment resistance in GBM. In glioma tumors derived from cells with acquired resistance to TMZ, lucanthone retains the ability to perturb tumor growth, inhibits autophagy by targeting lysosomes, and reduces Olig2 positivity. We also find that lucanthone may act as an inhibitor of palmitoyl protein thioesterase 1. Our results suggest that lucanthone may function as a potential treatment option for GBM tumors that are not amenable to TMZ treatment. SIGNIFICANCE STATEMENT We report that the antischistosome agent lucanthone impedes tumor growth in a preclinical model of temozolomide-resistant glioblastoma and reduces the numbers of stem-like glioma cells. In addition, it acts as an autophagy inhibitor, and its mechanism of action may be via inhibition of palmitoyl protein thioesterase 1. As there are no defined therapies approved for recurrent, TMZ-resistant tumor, lucanthone could emerge as a treatment for glioblastoma tumors that may not be amenable to TMZ both in the newly diagnosed and recurrent settings.

The eukaryotic protein kinase domain has been a broadly explored target for drug discovery, despite limitations imposed by its high sequence conservation as a shared modular domain and the development of resistance to drugs. One way of addressing those limitations has been to target its potential allosteric sites, shortly called allo-targeting, in conjunction with, or separately from, its conserved catalytic/orthosteric site that has been widely exploited. Allosteric regulation has gained importance as an alternative to overcome the drawbacks associated with the indiscriminate effect of targeting the active site, and it turned out to be particularly useful for these highly promiscuous and broadly shared kinase domains. Yet, allo-targeting often faces challenges as the allosteric sites are not as clearly defined as its orthosteric sites, and the effect on the protein function may not be unambiguously assessed. A robust understanding of the consequence of site-specific allo-targeting on the conformational dynamics of the target protein is essential to design effective allo-targeting strategies. Recent years have seen important advances in in silico identification of druggable sites and distinguishing among them those sites expected to allosterically mediate conformational switches essential to signal transmission. The present opinion underscores the utility of such computational approaches applied to the kinase domain, with the help of comparison between computational predictions and experimental observations.

This invited Team Profile was created by the Tiwary group, University of Maryland, College Park (USA) and the Seeliger group, Stony Brook University, New York (USA) . They recently published an article on the previously made observation through in‐cell screening that the blockbuster cancer drug Gleevec has the same binding affinity, yet different dissociation kinetics against wild‐type and N368S‐mutated Abl kinase. Through all‐atom enhanced molecular dynamics simulations guided by statistical mechanics and information theory, they were able to explain the mechanistic basis of this perplexing observation. Their work has ramifications for how pharmaceutical drugs might experience kinetic resistance due to mutations. “Protein Flexibility and Dissociation Pathway Differentiation Can Explain Onset of Resistance Mutations in Kinases”, M. Shekhar, Z. Smith, M. A. Seeliger, P. Tiwary, Angew. Chem. Int. Ed . 2022, e202200983 ; Angew. Chem . 2022, e202200983 .

Tyrosine kinases (TKs) play essential roles in signaling processes that regulate cell survival, migration, and proliferation. Dysregulation of tyrosine kinases underlies many disorders, including cancer, cardiovascular and developmental diseases, as well as pathologies of the immune system. Ack1 and Brk are nonreceptor tyrosine kinases (NRTKs) best known for their roles in cancer. Here, we have biochemically characterized novel Ack1 and Brk mutations identified in patients with systemic lupus erythematosus (SLE). These mutations are the first SLE-linked polymorphisms found among NRTKs. We show that two of the mutants are catalytically inactive, while the other three have reduced activity. To understand the structural changes associated with the loss-of-function phenotype, we solved the crystal structure of one of the Ack1 kinase mutants, K161Q. Furthermore, two of the mutated residues (Ack1 A156 and K161) critical for catalytic activity are highly conserved among other TKs, and their substitution in other members of the kinase family could have implications in cancer. In contrast to canonical gain-of-function mutations in TKs observed in many cancers, we report loss-of-function mutations in Ack1 and Brk, highlighting the complexity of TK involvement in human diseases.

Supplementary Figures 1-2 from Equally Potent Inhibition of c-Src and Abl by Compounds that Recognize Inactive Kinase Conformations

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. The high sequence and structural conservation of the catalytic kinase domain complicates the development of specific kinase inhibitors. As a consequence, most kinase inhibitors also inhibit off-target kinases which complicates the interpretation of phenotypic responses. Additionally, inhibition of off-targets may cause toxicity in patients. Therefore, highly selective kinase inhibition is a major goal in both biomedical research and clinical practice. Currently, efforts to improve selective kinase inhibition are dominated by the development of new kinase inhibitors. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target activities. We have developed a multicompound-multitarget scoring (MMS) method framework that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables rational polypharmacology by allowing optimization of inhibitor combinations against multiple selected on-targets and off-targets. Using MMS with previously published chemogenomic kinase inhibitor datasets we determine inhibitor combinations that achieve potent activity against a target kinase and that are more selective than the most selective single inhibitor against that target. We validate the calculated effect and selectivity of a combination of inhibitors using the in cellulo NanoBRET assay. The MMS framework is generalizable to other pharmacological targets where compound specificity is a challenge and diverse compound libraries are available.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicate the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound–multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Supplementary Methods and Materials from Equally Potent Inhibition of c-Src and Abl by Compounds that Recognize Inactive Kinase Conformations