Allopregnanolone Mediates Affective Switching Through Modulation of Oscillatory States in the Basolateral Amygdala

BACKGROUND

Brexanolone (allopregnanolone) was recently approved by the Food and Drug Administration for the treatment of postpartum depression, demonstrating long-lasting antidepressant effects. Despite our understanding of the mechanism of action of neurosteroids as positive allosteric modulators of GABA_A (gamma-aminobutyric acid A) receptors, we still do not fully understand how allopregnanolone exerts persistent antidepressant effects.

METHODS

We used electroencephalogram recordings in rats and humans along with local field potential, functional magnetic resonance imaging, and behavioral tests in mice to assess the impact of neurosteroids on network states in brain regions implicated in mood and used optogenetic manipulations to directly examine their relationship to behavioral states.

RESULTS

We demonstrated that allopregnanolone and synthetic neuroactive steroid analogs with molecular pharmacology similar to allopregnanolone (SGE-516 [tool compound] and zuranolone [SAGE-217, investigational compound]) modulate oscillations across species. We further demonstrated a critical role for interneurons in generating oscillations in the basolateral amygdala (BLA) and a role for δ-containing GABA_A receptors in mediating the ability of neurosteroids to modulate network and behavioral states. Allopregnanolone in the BLA enhances BLA high theta oscillations (6-12 Hz) through δ-containing GABA_A receptors, a mechanism distinct from other GABA_A positive allosteric modulators, such as benzodiazepines, and alters behavioral states. Treatment with the allopregnanolone analog SGE-516 protects mice from chronic stress-induced disruption of network and behavioral states, which is correlated with the modulation of theta oscillations in the BLA. Optogenetic manipulation of the network state influences the behavioral state after chronic unpredictable stress.

CONCLUSIONS

Our findings demonstrate a novel molecular and cellular mechanism mediating the well-established anxiolytic and antidepressant effects of neuroactive steroids.

Folate hydrolase-1 (FOLH1) is a novel target for antibody-based brachytherapy in Merkel cell carcinoma

Backgrounds

Folate Hydrolase‐1 (FOLH1; PSMA) is a type II transmembrane protein, luminally expressed by solid tumour neo‐vasculature. Monoclonal antibody (mAb), J591, is a vehicle for mAb‐based brachytherapy in FOLH1+ cancers. Brachytherapy is a form of radiotherapy that involves placing a radioactive material a short distance from the target tissue (e.g., on the skin or internally); brachytherapy is commonly accomplished with the use of catheters, needles, metal seeds and antibody or small peptide conjugates. Herein, FOLH1 expression in primary (p) and metastatic (m) Merkel cell carcinoma (MCC) is characterized to determine its targeting potential for J591‐brachytherapy.

Materials & Methods

Paraffin sections from pMCC and mMCC were evaluated by immunohistochemistry for FOLH1. Monte Carlo simulation was performed using the physical properties of conjugated radioisotope lutetium‐177. Kaplan–Meier survival curves were calculated based on patient outcome data and FOLH1 expression.

Results

Eighty‐one MCC tumours were evaluated. 67% (54/81) of all cases, 77% (24/31) pMCC and 60% (30/50) mMCC tumours were FOLH1+. Monte Carlo simulation showed highly localized ionizing tracks of electrons emitted from the targeted neo‐vessel. 42% (34/81) of patients with FOLH1+/− MCC had available survival data for analysis. No significant differences in our limited data set were detected based on FOLH1 status (p = 0.4718; p = 0.6470), staining intensity score (p = 0.6966; p = 0.9841) or by grouping staining intensity scores (− and + vs. ++, +++, +++) (p = 0.8022; p = 0.8496) for MCC‐specific survival or recurrence free survival, respectively.

Conclusions

We report the first evidence of prevalent FOLH1 expression within MCC‐associated neo‐vessels, in 60‐77% of patients in a large MCC cohort. Given this data, and the need for alternatives to immune therapies it is appropriate to explore the safety and efficacy of FOLH1‐targeted brachytherapy for MCC.

What's already known about this topic?

We report the first evidence of prevalent folate hydrolase‐1 (FOLH1; also known as prostate‐specific membrane antigen) expression within MCC‐associated neovessels.

What does this study add?

Herein, FOLH1 expression in Merkel cell carcinoma neovasculature is validated, and the therapeutic mechanism of specific, systemic targeting of disseminated disease with antibody‐based brachytherapy, is defined.

Preclinical characterization of zuranolone (SAGE-217), a selective neuroactive steroid GABAA receptor positive allosteric modulator

Zuranolone (SAGE-217) is a novel, synthetic, clinical stage neuroactive steroid GABA_A receptor positive allosteric modulator designed with the pharmacokinetic properties to support oral daily dosing. In vitro, zuranolone enhanced GABA_A receptor current at nine unique human recombinant receptor subtypes, including representative receptors for both synaptic (γ subunit-containing) and extrasynaptic (δ subunit-containing) configurations. At a representative synaptic subunit configuration, α₁β₂γ₂, zuranolone potentiated GABA currents synergistically with the benzodiazepine diazepam, consistent with the non-competitive activity and distinct binding sites of the two classes of compounds at synaptic receptors. In a brain slice preparation, zuranolone produced a sustained increase in GABA currents consistent with metabotropic trafficking of GABA_A receptors to the cell surface. In vivo, zuranolone exhibited potent activity, indicating its ability to modulate GABA_A receptors in the central nervous system after oral dosing by protecting against chemo-convulsant seizures in a mouse model and enhancing electroencephalogram β-frequency power in rats. Together, these data establish zuranolone as a potent and efficacious neuroactive steroid GABA_A receptor positive allosteric modulator with drug-like properties and CNS exposure in preclinical models. Recent clinical data support the therapeutic promise of neuroactive steroid GABA_A receptor positive modulators for treating mood disorders; brexanolone is the first therapeutic approved specifically for the treatment of postpartum depression. Zuranolone is currently under clinical investigation for the treatment of major depressive episodes in major depressive disorder, postpartum depression, and bipolar depression.

Dopamine D3/D2 Receptor Antagonist PF-4363467 Attenuates Opioid Drug-Seeking Behavior without Concomitant D2 Side Effects

Dopamine receptor antagonism is a compelling molecular target for the treatment of a range of psychiatric disorders, including substance use disorders. From our corporate compound file, we identified a structurally unique D3 receptor (D3R) antagonist scaffold, 1. Through a hybrid approach, we merged key pharmacophore elements from 1 and D3 agonist 2 to yield the novel D3R/D2R antagonist PF-4363467 (3). Compound 3 was designed to possess CNS drug-like properties as defined by its CNS MPO desirability score (≥4/6). In addition to good physicochemical properties, 3 exhibited low nanomolar affinity for the D3R (D3 K_i = 3.1 nM), good subtype selectivity over D2R (D2 K_i = 692 nM), and high selectivity for D3R versus other biogenic amine receptors. In vivo, 3 dose-dependently attenuated opioid self-administration and opioid drug-seeking behavior in a rat operant reinstatement model using animals trained to self-administer fentanyl. Further, traditional extrapyramidal symptoms (EPS), adverse side effects arising from D2R antagonism, were not observed despite high D2 receptor occupancy (RO) in rodents, suggesting that compound 3 has a unique in vivo profile. Collectively, our data support further investigation of dual D3R and D2R antagonists for the treatment of drug addiction.

Inositol Phosphate Accumulation in Vivo Provides a Measure of Muscarinic M1 Receptor Activation

The rationale for using M₁ selective muscarinic acetylcholine receptor activators for the treatment of cognitive impairment associated with psychiatric and neurodegenerative disease is well-established in the literature. Here, we investigate measurement of inositol phosphate accumulation, an end point immediately downstream of the M₁ muscarinic acetylcholine receptor signaling cascade, as an in vivo biochemical readout for M₁ muscarinic acetylcholine receptor activation. Five brain penetrant M₁-subtype selective activators from three structurally distinct chemical series were pharmacologically profiled for functional activity in vitro using recombinant cell calcium mobilization and inositol phosphate assays, and a native tissue hippocampal slice electrophysiology assay, to show that all five compounds presented a positive allosteric modulator agonist profile, within a narrow range of potencies. In vivo characterization using an amphetamine-stimulated locomotor activity behavioral assay and the inositol phosphate accumulation biochemical assay demonstrated that the latter has utility for assessing functional potency of M₁ activators. Efficacy measured by inositol phosphate accumulation in mouse striatum compared favorably to efficacy in reversing amphetamine-induced locomotor activity, suggesting that the inositol phosphate accumulation assay has utility for the evaluation of M₁ muscarinic acetylcholine receptor activators in vivo. The benefits of this in vivo biochemical approach include a wide response window, interrogation of specific brain circuit activation, an ability to model responses in the context of brain exposure, an ability to rank order compounds based on in vivo efficacy, and minimization of animal use.

Biological time series analysis using a context free language: applicability to pulsatile hormone data

We present a novel approach for analyzing biological time-series data using a context-free language (CFL) representation that allows the extraction and quantification of important features from the time-series. This representation results in Hierarchically AdaPtive (HAP) analysis, a suite of multiple complementary techniques that enable rapid analysis of data and does not require the user to set parameters. HAP analysis generates hierarchically organized parameter distributions that allow multi-scale components of the time-series to be quantified and includes a data analysis pipeline that applies recursive analyses to generate hierarchically organized results that extend traditional outcome measures such as pharmacokinetics and inter-pulse interval. Pulsicons, a novel text-based time-series representation also derived from the CFL approach, are introduced as an objective qualitative comparison nomenclature. We apply HAP to the analysis of 24 hours of frequently sampled pulsatile cortisol hormone data, which has known analysis challenges, from 14 healthy women. HAP analysis generated results in seconds and produced dozens of figures for each participant. The results quantify the observed qualitative features of cortisol data as a series of pulse clusters, each consisting of one or more embedded pulses, and identify two ultradian phenotypes in this dataset. HAP analysis is designed to be robust to individual differences and to missing data and may be applied to other pulsatile hormones. Future work can extend HAP analysis to other time-series data types, including oscillatory and other periodic physiological signals.

A frontal cortex event-related potential driven by the basal forebrain

Event-related potentials (ERPs) are widely used in both healthy and neuropsychiatric conditions as physiological indices of cognitive functions. Contrary to the common belief that cognitive ERPs are generated by local activity within the cerebral cortex, here we show that an attention-related ERP in the frontal cortex is correlated with, and likely generated by, subcortical inputs from the basal forebrain (BF). In rats performing an auditory oddball task, both the amplitude and timing of the frontal ERP were coupled with BF neuronal activity in single trials. The local field potentials (LFPs) associated with the frontal ERP, concentrated in deep cortical layers corresponding to the zone of BF input, were similarly coupled with BF activity and consistently triggered by BF electrical stimulation within 5-10 msec. These results highlight the important and previously unrecognized role of long-range subcortical inputs from the BF in the generation of cognitive ERPs. DOI: http://dx.doi.org/10.7554/eLife.02148.001.

Robot assisted laparoscopic prostatectomy in 2013

Robot assisted laparoscopic prostatectomy has surpassed open radical prostatectomy as the most common surgical approach for radical prostatectomy in the United States. In this article we briefly describe the evolution of this minimally invasive technique. The current diagnostic approaches of multiparametric magnetic resonance imaging and fusion biopsy used in preoperative workup of the patients are discussed, followed by a description of risk stratified a thermal nerve sparing approach with total anatomical reconstruction. Finally we present a critical appraisal of the published oncological, continence and potency outcomes.

Inhaled corticosteroid use in HIV-positive individuals taking protease inhibitors: a review of pharmacokinetics, case reports and clinical management

As a consequence of inhibition of the hepatic cytochrome P450 3A4 isozyme, treatment with HIV protease inhibitors can result in significant drug-drug interactions. One noteworthy interaction is between protease inhibitors and inhaled or intranasal corticosteroids. This interaction can result in adrenal insufficiency and iatrogenic Cushing's syndrome (with symptoms such as rapid weight gain, obesity, facial hirsutism and swelling), as well as hypertension, osteoporosis and decreased CD4 cell count. In this paper, we review and unite pharmacokinetic data, case reports and current research regarding this drug-drug interaction in order to suggest options for the clinical management of HIV-positive patients requiring treatment with protease inhibitors and inhaled or intranasal corticosteroids.

Visual Diagnosis: Pearling: a case study

We present the case of a patient who attempted to perform a type of body modification known as "pearling" or "genital beading" while in prison. This patient unfortunately caused severe trauma to his penis, requiring surgical intervention. Photographs of the traumatic injuries are presented.

Computing confidence intervals for point process models

Characterizing neural spiking activity as a function of intrinsic and extrinsic factors is important in neuroscience. Point process models are valuable for capturing such information; however, the process of fully applying these models is not always obvious. A complete model application has four broad steps: specification of the model, estimation of model parameters given observed data, verification of the model using goodness of fit, and characterization of the model using confidence bounds. Of these steps, only the first three have been applied widely in the literature, suggesting the need to dedicate a discussion to how the time-rescaling theorem, in combination with parametric bootstrap sampling, can be generally used to compute confidence bounds of point process models. In our first example, we use a generalized linear model of spiking propensity to demonstrate that confidence bounds derived from bootstrap simulations are consistent with those computed from closed-form analytic solutions. In our second example, we consider an adaptive point process model of hippocampal place field plasticity for which no analytical confidence bounds can be derived. We demonstrate how to simulate bootstrap samples from adaptive point process models, how to use these samples to generate confidence bounds, and how to statistically test the hypothesis that neural representations at two time points are significantly different. These examples have been designed as useful guides for performing scientific inference based on point process models.

Measuring instantaneous frequency of local field potential oscillations using the Kalman smoother

Rhythmic local field potentials (LFPs) arise from coordinated neural activity. Inference of neural function based on the properties of brain rhythms remains a challenging data analysis problem. Algorithms that characterize non-stationary rhythms with high temporal and spectral resolution may be useful for interpreting LFP activity on the timescales in which they are generated. We propose a Kalman smoother based dynamic autoregressive model for tracking the instantaneous frequency (iFreq) and frequency modulation (FM) of noisy and non-stationary sinusoids such as those found in LFP data. We verify the performance of our algorithm using simulated data with broad spectral content, and demonstrate its application using real data recorded from behavioral learning experiments. In analyses of ripple oscillations (100-250Hz) recorded from the rodent hippocampus, our algorithm identified novel repetitive, short timescale frequency dynamics. Our results suggest that iFreq and FM may be useful measures for the quantification of small timescale LFP dynamics.

Characterizing the dynamic frequency structure of fast oscillations in the rodent hippocampus

Fast oscillations or “ripples” are found in the local field potential (LFP) of the rodent hippocampus during awake and sleep states. Ripples have been found to correlate with memory related neural processing, however, the functional role of the ripple has yet to be fully established. We applied a Kalman smoother based estimator of instantaneous frequency (iFreq) and frequency modulation (FM) to ripple oscillations recorded in-vivo from region CA1 of the rat and mouse hippocampus during slow wave sleep. We found that (1) ripples exhibit stereotypical frequency dynamics that are consistent in the rat and mouse, (2) instantaneous frequency information may be used as an additional dimension in the classification of ripple events, and (3) the instantaneous frequency structure of ripples may be used to improve the detection of ripple events by reducing Type I and Type II errors. Based on our results, we propose that high temporal and spectral resolution estimates of frequency dynamics may be used to help elucidate the mechanisms of ripple generation and memory related processing.

Instantaneous frequency and amplitude modulation of EEG in the hippocampus reveals state dependent temporal structure

EEG and LFP activity reflect the dynamic and organized interactions of neural ensembles; therefore, it may be possible to use the features of brain rhythms to determine the computational state of a neuronal network. When neuronal networks are activated, physical principles predict that the frequency content of the field potential should reflect the network state, per se, and ergo the state transition. A novel way for characterizing brain states is by quantifying the temporal structure of AM and FM activity (change in amplitude and frequency over time) for brain rhythms of interest. The concept of AM and FM, in the quantitative sense, is virtually unexplored in systems neuroscience. This is not surprising considering estimation of FM activity requires fine temporal and precise estimation of instantaneous frequency. For AM activity, the absolute value of the Hilbert transform is sufficient. Here, we outline a practical pole tracking algorithm which uses a Kalman filter for univariate AR processes to estimate instantaneous frequency. We demonstrate the filter performance using simulated chirp and real EEG/LFP data recorded from the rat hippocampus; and show that AM/FM activity in EEG/LFP is temporally structured and dependent on behavioral and cognitive state. This algorithm has the potential to be a practical tool for characterizing fundamental structure in electrophysiology data and classifying computational states in the brain.

Micro-drive array for chronic in vivo recording: tetrode assembly

The tetrode, a bundle of four electrodes, has proven to be a valuable tool for the simultaneous recording of multiple neurons in-vivo. The differential amplitude of action potential signatures over the channels of a tetrode allows for the isolation of single-unit activity from multi-unit signals. The ability to precisely control the stereotaxic location and depth of the tetrode is critical for studying coordinated neural activity across brain regions. In combination with a micro-drive array, it is possible to achieve precise placement and stable control of many tetrodes over the course of days to weeks. In this protocol, we demonstrate how to fabricate and condition tetrodes using basic tools and materials, install the tetrodes into a multi-drive tetrode array for chronic in-vivo recording in the rat, make ground wire connections to the micro-drive array, and attach a protective cone onto the micro-drive array in order to protect the tetrodes from physical contact with the environment.

Micro-drive array for chronic in vivo recording: drive fabrication

Chronic recording of large populations of neurons is a valuable technique for studying the function of neuronal circuits in awake behaving rats. Lightweight recording devices carrying a high density array of tetrodes allow for the simultaneous monitoring of the activity of tens to hundreds of individual neurons. Here we describe a protocol for the fabrication of a micro-drive array with twenty one independently movable micro-drives. This device has been used successfully to record from hippocampal and cortical neurons in our lab. We show how to prepare a custom designed, 3-D printed plastic base that will hold the micro-drives. We demonstrate how to construct the individual micro-drives and how to assemble the complete micro-drive array. Further preparation of the drive array for surgical implantation, such as the fabrication of tetrodes, loading of tetrodes into the drive array and gold-plating, is covered in a subsequent video article.

A Bayesian decoding algorithm for analysis of information encoding in neural ensembles

Developing optimal strategies for constructing and testing decoding algorithms is an important question in computational neuroscience, In this field, decoding algorithms are mathematical methods that model ensemble neural spiking activity as they dynamically represent a biological signal. We present a recursive decoding algorithm based on a Bayesian point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by analyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from a rat foraging in an open circular environment The median decoding error during 10 minutes of open foraging was 5.5 cm, and the true coverage probability for 0.95 confidence regions was 0.75 using 32 neurons. These findings improve significantly on our previous results and suggest an approach to reading dynamically information represented in ensemble neural spiking activity.

Dynamic Analyses of Information Encoding in Neural Ensembles

Neural spike train decoding algorithms and techniques to compute Shan-non mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine inter-faces. Developing optimal strategies to desig n decoding algorithms and compute mutual information are therefore important problems in com-putational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the en-tropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probabil-ity of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the per-formance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our pre-vious results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

An application of reversible-jump Markov chain Monte Carlo to spike classification of multi-unit extracellular recordings

Multi-electrode recordings in neural tissue contain the action potential waveforms of many closely spaced neurons. While we can observe the action potential waveforms, we cannot observe which neuron is the source for which waveform nor how many source neurons are being recorded. Current spike-sorting algorithms solve this problem by assuming a fixed number of source neurons and assigning the action potentials given this fixed number. We model the spike waveforms as an anisotropic Gaussian mixture model and present, as an alternative, a reversible-jump Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the number of source neurons and to assign each action potential to a source. We derive this MCMC algorithm and illustrate its application using simulated three-dimensional data and real four-dimensional feature vectors extracted from tetrode recordings of rat entorhinal cortex neurons. In the analysis of the simulated data our algorithm finds the correct number of mixture components (sources) and classifies the action potential waveforms with minimal error. In the analysis of real data, our algorithm identifies clusters closely resembling those previously identified by a user-dependent graphical clustering procedure. Our findings suggest that a reversible-jump MCMC algorithm could offer a new strategy for designing automated spike-sorting algorithms.

An analysis of neural receptive field plasticity by point process adaptive filtering

Neural receptive fields are plastic: with experience, neurons in many brain regions change their spiking responses to relevant stimuli. Analysis of receptive field plasticity from experimental measurements is crucial for understanding how neural systems adapt their representations of relevant biological information. Current analysis methods using histogram estimates of spike rate functions in nonoverlapping temporal windows do not track the evolution of receptive field plasticity on a fine time scale. Adaptive signal processing is an established engineering paradigm for estimating time-varying system parameters from experimental measurements. We present an adaptive filter algorithm for tracking neural receptive field plasticity based on point process models of spike train activity. We derive an instantaneous steepest descent algorithm by using as the criterion function the instantaneous log likelihood of a point process spike train model. We apply the point process adaptive filter algorithm in a study of spatial (place) receptive field properties of simulated and actual spike train data from rat CA1 hippocampal neurons. A stability analysis of the algorithm is sketched in the. The adaptive algorithm can update the place field parameter estimates on a millisecond time scale. It reliably tracked the migration, changes in scale, and changes in maximum firing rate characteristic of hippocampal place fields in a rat running on a linear track. Point process adaptive filtering offers an analytic method for studying the dynamics of neural receptive fields.

Effective immunochemotherapy of human t(4;11) leukemia in mice with severe combined immunodeficiency (SCID) using B43 (anti-CD19)-pokeweed antiviral protein immunotoxin plus cyclophosphamide

Human mixed lineage leukemia cell line RS4;11 with the t(4;11)(q21;q23) translocation causes disseminated and invariably fatal leukemia in mice with severe combined immunodeficiency. Whereas an immunotoxin constructed from the murine anti-CD19(B43) monoclonal antibody and the plant toxin pokeweed antiviral protein (B43-PAP) has a potent in vitro anti-leukemic effect against clonogenic RS4;11 cells, its activity is further potentiated by the active cyclophosphamide congener mafosfamid. These intriguing observations prompted us to evaluate the in vivo antileukemic efficacy of combined immunochemotherapy employing B43-PAP immunotoxin plus cyclophosphamide against human t(4;11) leukemia cells in an RS4;11 severe combined immunodeficiency (SCID) mouse model system. Intravenous injections of B43-PAP or cyclophosphamide improved survival of SCID mice challenged with RS4;11 leukemia, as reflected by markedly prolonged median survival times. After intravenous inoculation of 5 x 10(7) RS4;11 leukemia cells, the median survival times were 41 days for saline-treated control mice (n = 12), 44 days for control mice treated with unconjugated B43 monoclonal antibody and PAP (n = 12), 56 days for mice treated with the control immunotoxin G17.2 (anti-CD4)-PAP (n = 6), 79 days for B43-PAP-treated test mice (n = 12), and 80 days for cyclophosphamide-treated test mice (n = 16). Notably, combined immunochemotherapy using B43-PAP plus cyclophosphamide was significantly more effective than either B43-PAP or cyclophosphamide alone. The median survival time for a total of 22 SCID mice undergoing combined immunochemotherapy with B43-PAP followed by cyclophosphamide (n = 12) or cyclophosphamide followed B43-PAP (n = 10) was > 150 days. The Kaplan-Meier estimates and standard errors of the probability of event-free survival at 5 months after inoculation of 5 x 10(7) RS4;11 cells were 21 +/- 13% for B43-PAP-treated mice, 7 +/- 6% for cyclophosphamide-treated mice, 90 +/- 10% for mice treated with B43-PAP followed by cyclophosphamide (n = 12), and 90 +/- 10% for mice treated with cyclophosphamide followed by B43-PAP (n = 10). Our results lead us to recommend that initial consideration be given to combined immunochemotherapy protocols using B43-PAP immunotoxin plus cyclophosphamide for treatment of refractory or relapsed t(4;11) leukemias.