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We disclose novel amphiphilic ruthenium and osmium complexes that auto-assemble into nanomedicines with potent antiproliferative activity by inhibition of mitochondrial respiration. The self-assembling units were rationally designed from the [M(p-cymene)(1,10-phenanthroline)Cl]PF(6) motif (where M is either Ru(II) or Os(II)) with an appended C(16) fatty chain to achieve high cellular activity, nano-assembling and mitochondrial targeting. These amphiphilic complexes block cell proliferation at the sub-micromolar range and are particularly potent towards glioblastoma neurospheres made from patient-derived cancer stem cells. A subcutaneous mouse model using these glioblastoma stem cells highlights one of our C(16) Os(II) nanomedicines as highly successful in vivo. Mechanistically, we show that they act as metabolic poisons, strongly impairing mitochondrial respiration, corroborated by morphological changes and damage to the mitochondria. A genetic strategy based on RNAi gave further insight on the potential involvement of microtubules as part of the induced cell death. In parallel, we examined the structural properties of these new amphiphilic metal-based constructs, their reactivity and mechanism.

Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021.

Ruthenium (Ru) and osmium (Os) complexes are of sustained interest in cancer research and may be alternative to platinum-based therapy. We detail here three new series of ruthenium and osmium complexes, supported by physico-chemical characterizations, including time-dependent density functional theory, a combined experimental and computational study on the aquation reactions and the nature of the metal-arene bond. Cytotoxic profiles were then evaluated on several cancer cell lines although with limited success. Further investigations were, however, performed on the most active series using a genetic approach based on RNA interference and highlighted a potential multi-target mechanism of action through topoisomerase II, mitotic spindle, HDAC and DNMT inhibition., info:eu-repo/semantics/published

Small molecules can affect many cellular processes. The disambiguation of these effects to identify the causative mechanisms of cell death is extremely challenging. This challenge impacts both clinical development and the interpretation of chemical genetic experiments. CX-5461 was developed as a selective RNA polymerase I inhibitor, but recent evidence suggests that it may cause DNA damage and induce G-quadraplex formation. Here we use three complimentary data mining modalities alongside biochemical and cell biological assays to show that CX-5461 exerts its primary cytotoxic activity through topoisomerase II poisoning. We then show that acquired resistance to CX-5461 in previously sensitive lymphoma cells confers collateral resistance to the topoisomerase II poison doxorubicin. Doxorubicin is already a frontline chemotherapy in a variety of hematopoietic malignancies, and CX-5461 is being tested in relapse/refractory hematopoietic tumors. Our data suggest that the mechanism of cell death induced by CX-5461 is critical for rational clinical development in these patients. Moreover, CX-5461 usage as a specific chemical genetic probe of RNA polymerase I function is challenging to interpret. Our multimodal data-driven approach is a useful way to detangle the intended and unintended mechanisms of drug action across diverse essential cellular processes.

In the United States, state-level re-openings in spring 2020 presented an opportunity for the resurgence of SARS-CoV-2 transmission. One important question during this time was whether human contact and mixing patterns could increase gradually without increasing viral transmission, the rationale being that new mixing patterns would likely be associated with improved distancing, masking, and hygiene practices. A second key question to follow during this time was whether clinical characteristics of the epidemic would improve after the initial surge of cases. Here, we analyze age-structured case, hospitalization, and death time series from three states – Rhode Island, Massachusetts, and Pennsylvania – that had successful reopenings in May 2020 without summer waves of infection. Using a Bayesian inference framework on eleven daily data streams and flexible daily population contact parameters, we show that population-average mixing rates dropped by >50% during the lockdown period in March/April, and that the correlation between overall population mobility and transmission-capable mixing was broken in May as these states partially re-opened. We estimate the reporting rates (fraction of symptomatic cases reporting to health system) at 96.3% (RI), 62.5% (MA), and 98.9% (PA). We show that elderly individuals were less able to reduce contacts during the lockdown period when compared to younger individuals, leading to the outbreak being concentrated in elderly congregate settings despite the lockdown. Attack rate estimates through August 31 2020 are 6.2% (95% CI: 5.7% – 6.8%) of the total population infected for Rhode Island, 6.7% (95% CI: 5.4% – 7.6%) in Massachusetts, and 2.7% (95% CI: 2.5% – 3.1%) in Pennsylvania, with some validation available through published seroprevalence studies. Infection fatality rates (IFR) estimates are higher in our analysis (>2%) than previously reported values, likely resulting from the epidemics in these three states affecting the most vulnerable sub-populations and the close matches between the states’ reported COVID-19 deaths and excess deaths. Data in Pennsylvania may have been underreported for both non-hospitalized and hospitalized patients, casting substantial uncertainty on estimates of attack rate and infection fatality rate.

In emergency epidemic and pandemic settings, public health agencies need to be able to measure the population-level attack rate, defined as the total percentage of the population infected thus far. During vaccination campaigns in such settings, public health agencies need to be able to assess how much the vaccination campaign is contributing to population immunity; specifically, the proportion of vaccines being administered to individuals who are already seropositive must be estimated.To estimate population-level immunity to SARS-CoV-2 through May 31, 2021, in Rhode Island, Massachusetts, and Connecticut.This observational case series assessed cases, hospitalizations, intensive care unit occupancy, ventilator occupancy, and deaths from March 1, 2020, to May 31, 2021, in Rhode Island, Massachusetts, and Connecticut. Data were analyzed from July 2021 to November 2021.COVID-19-positive test result reported to state department of health.The main outcomes were statistical estimates, from a bayesian inference framework, of the percentage of individuals as of May 31, 2021, who were (1) previously infected and vaccinated, (2) previously uninfected and vaccinated, and (3) previously infected but not vaccinated.At the state level, there were a total of 1 160 435 confirmed COVID-19 cases in Rhode Island, Massachusetts, and Connecticut. The median age among individuals with confirmed COVID-19 was 38 years. In autumn 2020, SARS-CoV-2 population immunity (equal to the attack rate at that point) in these states was less than 15%, setting the stage for a large epidemic wave during winter 2020 to 2021. Population immunity estimates for May 31, 2021, were 73.4% (95% credible interval [CrI], 72.9%-74.1%) for Rhode Island, 64.1% (95% CrI, 64.0%-64.4%) for Connecticut, and 66.3% (95% CrI, 65.9%-66.9%) for Massachusetts, indicating that more than 33% of residents in these states were fully susceptible to infection when the Delta variant began spreading in July 2021. Despite high vaccine coverage in these states, population immunity in summer 2021 was lower than planned owing to an estimated 34.1% (95% CrI, 32.9%-35.2%) of vaccines in Rhode Island, 24.6% (95% CrI, 24.3%-25.1%) of vaccines in Connecticut, and 27.6% (95% CrI, 26.8%-28.6%) of vaccines in Massachusetts being distributed to individuals who were already seropositive.These findings suggest that future emergency-setting vaccination planning may have to prioritize high vaccine coverage over optimized vaccine distribution to ensure that sufficient levels of population immunity are reached during the course of an ongoing epidemic or pandemic.

Summary Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Mutual exclusivity has been an empirically useful signal for identifying activating mutations that respond to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. We apply this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI that was suggested to predict sensitivity to ALK inhibitors and we find that it is not mutually exclusive with key melanoma oncogenes. Furthermore, we find that ALKATI is not likely to be sufficient for cellular transformation or growth, and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment., Graphical abstract, Highlights • A method to test rare genomic findings for their relative conditional selection • ALKATI is not as mutually exclusive with BRAF or NRAS as they are with each other • ALKATI is not likely to be sufficient for cellular transformation or growth in vitro • Expressing activated oncogenic ALK in BRAFV600E melanoma cells is cytotoxic, Biological sciences; Systems biology; Cancer systems biology

Targeted therapies that are rationally designed to inhibit driver oncogenes generally offer more durable responses for cancer patients compared to standard chemotherapies. However, in virtually all cases, the development of drug resistance inevitably subverts eradication attempts. In tyrosine kinases inhibitors (TKIs), resistance is often addressed with next-generation TKIs or combination therapies. Appropriate treatment strategies are selected based on the nature of resistance outgrowth. However, intra- and intertumoral heterogeneity makes it impossible to predict a priori which resistance variants will drive relapse in a given patient, and thus what therapeutic strategy will be optimal. Here, we propose a resistance-agnostic treatment approach that seeks to leverage evolutionary principles, rather than combat them. We designed and demonstrated the capability of a gene therapy, which we term “dual-switch selection gene drives”, that can be used to engineer cancer cells in order to guide the tumor’s evolutionary trajectory towards eradication. The dual-switch gene drive system is a genetic circuit composed of two genes that can be delivered to tumor cells on a single vector in situ. The “switch one” gene is a drug target analog that acts as an inducible, transient resistance marker, enabling rapid selection of modified cells. The “switch two” gene is the therapeutic payload of the system. Its role is to kill tumor cells by activating an inert small molecule. Importantly, the activity of the therapeutic gene is diffuse, and this bystander effect is maximized by hitchhiking off the inducible resistance gene. We developed stochastic dynamic models of tumor evolution to predict parameter regimes where eradication is possible. With these design criteria, we constructed and evaluated our system. First, we generated a panel of optimized “switch one” genes co-opting the kinase domains for a range of TKI targets, including EGFR, ALK, and RET. We demonstrated that these synthetic genes could induce a conditional resistance phenotype and be used to select for modified cells in a mixed population. Next, we evaluated the suicide gene cytosine deaminase (CD), which converts the inert prodrug 5-FC into the nucleoside analog 5-FU. Cell viability assays for mixed populations of wild-type and CD-expressing cells confirmed a strong bystander effect in this system. Finally, we assembled a single vector harboring both switches. In a pooled experiment, gene drive cells were able to drive the mixed population to eradication, even when pre-existing resistance to the TKI was introduced. Our dual-switch design demonstrates the potential to target pre-existing resistant subpopulations, without foreknowledge of the exact nature of resistance. Given that drug resistance, and thus treatment failure, is inevitable in most cancers, our treatment strategy enables us to decide the terms of relapse. Rather than combat drug resistance, we can leverage it to amplify a therapeutic gene’s effect and ultimately collapse a complex tumor population. Citation Format: Scott M. Leighow, Haider Inam, Justin R. Pritchard. Design and construction of evolutionary-guided "selection gene drive" therapy [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr PR014.

This abstract is being presented as a short talk in the scientific program. A full abstract is available in the Proffered Abstracts section (PR014) of the Conference Proceedings. Citation Format: Scott M. Leighow, Haider Inam, Justin R. Pritchard. Design and construction of evolutionary-guided "selection gene drive" therapy [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B007.

Systematically scanning the effects of amino acid substitutions across a protein is a powerful technique in protein engineering, synthetic biology, and the study of drug resistance. In general, these selection experiments increase growth rates to select for mutants. Current methods use molecular barcodes to circumvent the resolution barriers imposed by conventional NGS sequencing of point mutations in mutant pools. Barcodes introduce synonymous mutations in the genome and assume neutrality, but it is now well accepted that synonymous mutations affect protein function. In a different approach, barcodes on cDNA constructs require a cumbersome second cloning step during library generation. We describe an approach that harnesses a highly sensitive duplex sequencing strategy to skip this barcode integration step and directly measure the target in label-free deep mutant pools. Furthermore, we employ the use of single-mutant standards with well-studied growth dynamics. By tracking the deviations from the expected growth trajectory of these mutant standards, we correct for dose-response variations that often result in low replicate agreement in these functional screens. Our workflow is compatible with any pool generation strategy, and the parameters that we obtain are robust enough to be useful in detailed models of cellular dynamics in synthetic biology studies. We developed a method for the functional assessment of deep mutant pools in a single pooled experiment. The workflow includes generating a label-free library of mutants at a desired target and sequencing the mutagenized pool before and during drug treatment using duplex sequencing. We demonstrate our pooled approach by focusing on target-driven resistance in Chronic Myeloid Leukemia (CML). To this end, we studied the 17 most clinically abundant BCRABL resistant mutations in imatinib refractory CML at low frequencies of 1 in 15,000. The sensitivity afforded by duplex sequencing enabled the accurate quantification of growth rates of these mutants under imatinib selection, with all 17 mutants closely matching their expected growth trajectory as predicted by individual dose-response studies. Moreover, we use an inference-based approach that uses hill-curve information from our mutant standards to detect slight variations in attempted dose between pooled replicates. Correcting for these slight variations in attempted dose leads to a >10-fold improvement in replicate agreement. Here, we demonstrate a fast, label-free workflow that achieves high levels of sensitivity and specificity in measuring growth dynamics of deep mutant pools. Importantly, our method does not rely on synonymous, potentially error-inducing labels. Given recent successes in deep learning that predict structure and function in proteins, our approach can be used to generate large training sets of mutation-drug phenotypes. Predicting the rich phenotypic data generated using existing sequence and structural data could dramatically reduce the experiments that are necessary to generate predictive information on drug resistance. Citation Format: Haider Inam, Scott Leighow, Justin Pritchard. Massively parallel functional assessment of label-free mutant pools is a universal approach to parametrize mechanistic models of drug resistance evolution [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B014.

Background When three SARS-CoV-2 vaccines came to market in Europe and North America in the winter of 2020–2021, distribution networks were in a race against a major epidemiological wave of SARS-CoV-2 that began in autumn 2020. Rapid and optimized vaccine allocation was critical during this time. With 95% efficacy reported for two of the vaccines, near-term public health needs likely require that distribution is prioritized to the elderly, health care workers, teachers, essential workers, and individuals with comorbidities putting them at risk of severe clinical progression. Methods We evaluate various age-based vaccine distributions using a validated mathematical model based on current epidemic trends in Rhode Island and Massachusetts. We allow for varying waning efficacy of vaccine-induced immunity, as this has not yet been measured. We account for the fact that known COVID-positive cases may not have been included in the first round of vaccination. And, we account for age-specific immune patterns in both states at the time of the start of the vaccination program. Our analysis assumes that health systems during winter 2020–2021 had equal staffing and capacity to previous phases of the SARS-CoV-2 epidemic; we do not consider the effects of understaffed hospitals or unvaccinated medical staff. Results We find that allocating a substantial proportion (>75%) of vaccine supply to individuals over the age of 70 is optimal in terms of reducing total cumulative deaths through mid-2021. This result is robust to different profiles of waning vaccine efficacy and several different assumptions on age mixing during and after lockdown periods. As we do not explicitly model other high-mortality groups, our results on vaccine allocation apply to all groups at high risk of mortality if infected. A median of 327 to 340 deaths can be avoided in Rhode Island (3444 to 3647 in Massachusetts) by optimizing vaccine allocation and vaccinating the elderly first. The vaccination campaigns are expected to save a median of 639 to 664 lives in Rhode Island and 6278 to 6618 lives in Massachusetts in the first half of 2021 when compared to a scenario with no vaccine. A policy of vaccinating only seronegative individuals avoids redundancy in vaccine use on individuals that may already be immune, and would result in 0.5% to 1% reductions in cumulative hospitalizations and deaths by mid-2021. Conclusions Assuming high vaccination coverage (>28%) and no major changes in distancing, masking, gathering size, hygiene guidelines, and virus transmissibility between 1 January 2021 and 1 July 2021 a combination of vaccination and population immunity may lead to low or near-zero transmission levels by the second quarter of 2021. Supplementary Information The online version contains supplementary material available at (10.1186/s12916-021-02038-w).

Genomic data can facilitate personalized treatment decisions by enabling therapeutic hypotheses in individual patients. Conditional selection, which includes mutual exclusivity, is a signal that has been empirically useful for identifying mutations that may be sensitive to single agent targeted therapies. However, a low mutation frequency can underpower this signal for rare variants and prevent robust conclusions from genomic data. We develop a resampling based method for the direct pairwise comparison of conditional selection between sets of gene pairs. This effectively creates positive control guideposts of mutual exclusivity in known driver genes that normalizes differences in mutation abundance. We applied this method to a transcript variant of anaplastic lymphoma kinase (ALK) in melanoma, termed ALKATI, which has been the subject of a recent controversy in the literature. We reproduced some of the original cell transformation experiments, performed rescue experiments, and analyzed drug response data to revisit the original ALKATI findings. We found that ALKATI is not as mutually exclusive with BRAF or NRAS as BRAF and NRAS genes are with each other. We performed in vitro transformation assays and rescue assays that suggested that alternative transcript initiation in ALK is not likely to be sufficient for cellular transformation or growth and it does not predict single agent therapeutic dependency. Our work strongly disfavors the role of ALKATI as a targetable oncogenic driver that might be sensitive to single agent ALK treatment. The progress of other experimental agents in late-stage melanoma and our experimental and computational re-analysis led us to conclude that further single agent testing of ALK inhibitors in patients with ALKATI should be limited to cases where no other treatment hypotheses can be identified.

SummaryTheoretical treatments of evolutionary dynamics tend to model the probability that a single “resistant” species will arise in a population. However, experimental studies have identified a diversity of mutations that can lead to genetic resistance. By quantitatively predicting mutations that occur across an entire drug target during treatment, we identify and bridge a fundamental gap in drug resistance theory: that nucleotide/codon substitution biases can dictate which resistant variants arise in the clinic. We find that the likeliest mutation can beat the most resistant mutation. This creates a new paradigm in drug resistance that we term“survival of the likeliest”. We use epidemiological evidence in leukemia, isogenic experiments, stochastic dynamics, and large-scale simulations to support this theory. In addition, this work has strong implications for drug design because not all resistance liabilities are created equal. In pathogenic populations that exhibit survival of the likeliest, exploiting the least likely evolutionary path can minimize resistance across a population during widespread drug use, even when a vulnerability-free molecule or combination cannot be made.Data and Code Availabilityhttps://github.com/pritchardlabatpsu/SurvivalOfTheLikeliest/

SUMMARY Rationally designing drugs that last longer in the face of biological evolution is a critical objective of drug discovery. However, this goal is thwarted by the diversity and stochasticity of evolutionary trajectories that drive uncertainty in the clinic. Although biophysical models can qualitatively predict whether a mutation causes resistance, they cannot quantitatively predict the relative abundance of resistance mutations in patient populations. We present stochastic, first-principle models that are parameterized on a large in vitro dataset and that accurately predict the epidemiological abundance of resistance mutations across multiple leukemia clinical trials. The ability to forecast resistance variants requires an understanding of their underlying mutation biases. Beyond leukemia, a meta-analysis across prostate cancer, breast cancer, and gastrointestinal stromal tumors suggests that resistance evolution in the adjuvant setting is influenced by mutational bias. Our analysis establishes a principle for rational drug design: when evolution favors the most probable mutant, so should drug design., Graphical Abstract, In Brief Drug resistance is often addressed through next-generation drug design, but evolutionary diversity complicates these efforts. Here, Leighow et al. demonstrate that multi-scale models can quantitatively predict mutant frequency. We find that when heterogeneity is limited, analysis requires an understanding of substitution likelihood. We show that these models can inform evolutionarily optimized drug design.